integrating corpus-linguistic and conversation … of social action in interaction’’ ... in...

ORIGINAL PAPER

Integrating Corpus-Linguistic and Conversation-Analytic Transcription in XML: The Caseof Backchannels and Overlap in StorytellingInteraction

Christoph Ruhlemann1

Received: 15 November 2016 / Accepted: 31 May 2017 / Published online: 15 June 2017

� Springer International Publishing AG 2017

Abstract This paper sketches out and illustrates the research opportunities that

come with the recent addition to BNCweb of very large numbers of audio files for

the spoken component in the BNC. It aims to demonstrate that the availability of the

audio files enables researchers not only to correct the orthographic transcripts, but

also to re-transcribe the conversations using conversation-analytic transcription. It

also shows that the CA transcripts can be integrated into the BNC’s XML anno-

tation network and illustrates how XML query tools such as XPath and XQuery can

be used to efficiently exploit the XML network. The main thrust of the paper is to

argue that the integration of corpus-linguistic and conversation-analytic transcrip-

tion in XML can make major contributions both to CL and CA. CL research into

conversation can for the first time be performed on the basis of transcription that is

‘‘detailed enough to facilitate the analyst’s quest to discover and describe orderly

practices of social action in interaction’’ (Hepburn and Bolden, in: Sidnell, Stivers

(eds) The handbook of conversation analysis, Wiley Malden, 2013: 58) while CA

research can gain a large-scale quantitative basis to substantiate claims about the

generalizability of observed regularities and patterns in talk-in-interaction. To

illustrate the benefits of doing research on re-transcriptions of the BNC’s audio files,

a case study is presented on backchannels occurring in overlap in storytelling

interaction. The case study reveals, inter alia, that backchannels produced by story

recipients simultaneously with parts of the storyteller’s ongoing turn tend to

increase in frequency as the storytelling reaches its climax. Backchannel overlap is

thus in synchrony with story organization. This finding adds weight to Goodwin’s

observation that recipients attend to the task ‘‘not simply of listening to the events

being recounted but rather of distinguishing different subcomponents of the talk in

terms of the alternative possibilities for action they invoke’’ (Goodwin, in:

& Christoph Ruhlemann

[email protected]

1 University of Marburg, Marburg, Germany

123

Corpus Pragmatics (2017) 1:201–232

DOI 10.1007/s41701-017-0018-7

http://crossmark.crossref.org/dialog/?doi=10.1007/s41701-017-0018-7&domain=pdf

http://crossmark.crossref.org/dialog/?doi=10.1007/s41701-017-0018-7&domain=pdf

Atkinson, Heritage (eds) Structures of social action: studies in conversation anal-

ysis, Cambridge University Press, Cambridge, 1984: 243). The case study also

presents exploratory evidence to suggest that, arguably due to the extended length of

storytelling turns (Ochs and Capps in Living narrative, Harvard University Press,

Cambridge, 2001), the proportion of overlap in running speech may be considerably

lower in storytelling than in general conversation and telephone conversation.

Keywords BNC � BNCweb � Conversation-analytic transcription � XML � XPath �XQuery � Corpus pragmatics � Backchannels � Overlap � Storytelling

Introduction

The British National Corpus (BNC) is no doubt a remarkable success story as it

probably represents the most widely-used corpus in corpus-linguistic research. A

large contribution to its success has been the creation of BNCweb, a web interface

for the corpus (Hoffmann et al. 2008). BNCweb recently added yet another

achievement to its list of achievements: a substantial number of the audio recordings

on which the transcripts are based were made available (Coleman et al. 2012).

Making available the audio files underyling a spoken corpus does have some

tradition as far as languageas other than English are concerned; see, for example, the

Spoken Dutch Corpus project (1998–2004) (cf. http://lands.let.ru.nl/cgn/doc_

English/topics/project/pro_info.htm) for Dutch as well as the C-ORAL-ROM cor-

pus (2001–2004) for the four main Romance languages (Cresti and Moneglia 2005)

with the language data presented in multimedia format, allowing simultaneous

access to aligned acoustic and textual information.1 For spoken English corpora, the

BNC audio files are a rare exception. The availability of the audio files presents an

extraordinary new research opportunity with important implications for both Corpus

Linguistics (CL) and Conversation Analysis (CA). In this paper the aim is to sketch

out this opportunity.

To start with, transcription errors in the text can be corrected thus improving the

textual accuracy. Also, speakers, up until recently hidden behind speaker ID tags,

come to life, their voices can be heard and speech delivery can now be examined

first-hand: voice quality and its modulation in mimicry, changes in volume, shifts in

pitch, slowing down or speeding up—everything is out in the open. Finally,

characteristics of timing and sequencing can be determined: pauses can be measured

down to split seconds, latching can be detected, overlap can be ascertained beyond

doubt. In other words: there exists now a resource that invites and facilitates the

kind of fine-grained transcription that Conversation Analysis has made its hallmark.

Considering that the audio files record hundreds of hours of conversations involving

very large numbers of very diverse speakers, the potential for conversation-analytic

research is immense and probably, since it is all publicly available, without parallel.

What is more, the audio files come with already-complete orthographic transcripts,

1 The C-ORAL-ROM project started a tradition continued by Brazilian Portuguese, C-ORAL-BRASIL;

Japanese, C-ORAL-JAPAN; and Chinese C-ORAL-CHINA.

202 C. Ruhlemann

123

http://lands.let.ru.nl/cgn/doc_English/topics/project/pro_info.htm

http://lands.let.ru.nl/cgn/doc_English/topics/project/pro_info.htm

so the conversations need not be transcribed from scratch. What remains to be done

is re-transcribing the conversations to weed out erroneous transcription and align

the transcripts with CA conventions (see ‘‘Appendix’’). This requires still a lot of

work but far less work than a new transcription. Finally, for both the audio data and

their transcripts, BNC’s XML architecture is in place providing meta-data related to

speakers (age, sex, class, etc.), speaking turns (delimited by\u[ elements), and

Part-of-Speech (PoS) (morpho-syntactic analysis of word-class) for each and every

word form. That is, altogether three resources are available: the audio files, the

transcripts (initially orthographic and, after re-transcription, in CA format), plus the

BNC’s XML scaffolding. If XML is used for the orthographic-transcripts-turned-

CA-transcripts it will be possible to integrate CA-style transcription in the format of

the eXtensible Mark-up Language XML [for an introduction to XML for corpus

linguists see Hardie (2014); for a general introduction, see, for example, Watt

(2002)]. XML ‘‘provides a standard syntax for the mark-up of data and documents’’

(Watt 2002: 1). Its defining feature is the network structure where every node is

connected in some way to any other node in the document and where thus any node

or set of nodes can be addressed and extracted using appropriate XML query

languages such as XPath and XQuery (cf. Watt 2002; Walmsley 2007; Ruhlemann

and Gries 2015).

The integration of corpus-linguistic and conversation-analytic transcription in

XML will make important contributions both to corpus-linguistic and conversation-

analytic research.

CL will benefit from the integration because not only will its data base be

improved in the sense that the re-transcription of the audio files will help purify the

textual record. The corpus data will also become much richer in the sense that the

rudimentary paralinguistic information already available in the BNC, referred to as

‘enhanced orthographic’ (Crowdy 1994: 26), will be augmented significantly by the

rich phonological and interactional minutiae able ‘‘to facilitate the analyst’s quest to

discover and describe orderly practices of social action in interaction’’ (Hepburn and

Bolden 2013: 58). In other words: talk and talk-in-interaction will become

graspable, and researchable, in a resolution rarely achieved in CL research [for a

similarly fine-grained CL annotation project, cf. SPICE-Ireland, a spoken corpus of

Irish English densely annotated for prosody, discourse markers, quotatives,

quotations, and speech acts but not available in XML format (Kallen and Kirk

2012)].

The benefits of integrating corpus-linguistic and conversation-analytic annotation

in XML will be no less tangible for CA research. This is because the XML format

provides a structure that is searchable and extractable with great efficiency using

XML query tools such as XPath and XQuery (for an introduction for corpus

linguists see Ruhlemann et al. 2015; for an application in learner corpora, see

Campillos 2014). The XML format also has been a mandatory feature since 2004 in

the above-mentioned C-ORAL-ROM corpora. The precise searchability and

extractability of CA transcripts encoded in XML will add to CA, typically defined

as a qualitative method (e.g., Stivers and Sidnell 2013: 2), a serious quantitative

component. This quantitative component will come timely as the reliance on the

qualitative method alone has recently been questioned within CA. Stivers (2015),

Integrating Corpus-Linguistic and Conversation-Analytic… 203

123

for example, criticizes the reduction of CA to a merely qualitative method as ‘‘a

very restrictive view of CA’’ (Stivers 2015: 16). Intriguingly, the reduction also runs

counter to fundamental assumptions of CA. One such assumption is Sacks’s ‘‘order

at all points’’ (Sacks 1984: 22) with ‘order’ understood as a ‘‘resource of a culture’’

(Sacks 1984: 22). Based on the notion of order, CA attempts to describe social

practices of action. ‘Practices of action’ involve ‘‘communication rules that

generate regular patterns of understanding and interactional organization’’ (Robin-

son 2007: 65; emphasis in original). Regular patterns, by definition, require

recurrence; recurrence requires quantification. In fact, quantification has long been a

standard ingredient of CA research. While CA ‘‘does not generally report precise

numbers, most papers in this tradition do rely on [scalar] descriptors’’ (Stivers 2015:

6), such as ‘massively’, ‘quite common’, ‘a lot more frequent’, etc. However, scalar

terms are not only inherently vague leaving open the exact amount of a distribution:

How massively? How common? How much more frequent? More importantly, they

are also unable to help the researcher address the crucial question, raised by Sacks

(1984: 23) in the context of speaking of ‘order at all points’, of ‘generalizability’,

the key question of inferential statistics: Is the distribution I observe in my small

sample the same as the distribution in the population of the phenomenon under

investigation? Can I justifiably generalize from sample to population? Answering

this question requires ‘‘numbers and statistics’’ (Robinson 2007: 65). The XML

format for CA transcriptions facilitates these numbers and helps lay the foundations

for descriptive and analytic statistics. Thus, the availability of CA transcripts in

XML opens up the possibility of supporting claims about social practices of action

in talk-in-interaction on a statistically sound basis.

In the following I illustrate the procedure of re-transcribing audio-based BNC

data as CA transcription and integrating the conversation-analytic details into the

BNC’s existing annotational architecture. The description traces the path from the

orthographic transcript (Section ‘‘Orthographic Transcript’’) to the CA transcript

(Section ‘‘CA Transcript’’) and, finally to the CA transcript integrated with the

BNC’s PoS tagging structure in XML (Section ‘‘XML Transcript’’). In Sec-

tion ‘‘XML Transcript’’, I also provide some simple XPath queries for the sample;

the aim here is to illustrate the potential of this technology for extracting data from

densely annotated XML documents and providing the raw data for statistical

analysis. In Section ‘‘Case Study: Backchannels and Overlap in Storytelling

Interaction’’, a case study is presented on backchannels and overlap in storytelling.

Orthographic Transcript

The text chosen for illustrative purposes is a short storytelling from the BNC file

KBD. The telling is part of an extended round of stories (cf. Sacks 1992)

thematically related to unlucky fishing experiences. Excerpt (1) is the orthographic

transcript downloaded fom BNCweb; lines are numbered for ease of reference, the

numbers between line numbers and text represent counts of s-units in the file,\-|-[denotes the boundaries of overlapped speech; finally, arrows are used to draw

attention to special features of the transcript:

204 C. Ruhlemann

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Even a cursory look through the excerpt raises a few questions: in line 2 the

second overlap delimitor \-|-[ is curiously missing. The same absence can be

observed in lines 12 and 19. The absence is explained by the fact that some tags got

lost in the course of the BNC’s conversion from SGML to XML. These losses

affected mostly instances of overlap and pauses (cf. Hoffmann et al. 2008: 57).

Given the faulty annotation, automatic retrieval of these instances of overlap is

impossible. Further, the transcript records nine pauses; the duration (6 seconds) is

given only for one of them, in line 20.

CA Transcript

The following is an audio-based CA transcript of the same storytelling passage

using Jeffersonian transcription symbols (for CA transcription conventions see, for

example, Liddicott 2007; Schegloff 2000; also, see the list of relevant transcription

symbols in the ‘‘Appendix’’).

(2) [“Drained canal”, BNC: KBD 1790-1801]

Alan 1 Well it's, it's (.) luck innit [( I don' know), ] Barry 2 [ I remember ] once go:n' on, 3 I got- (0.4) we got up 'bou' three three thirty in the morning 4 ( ) went out to er (0.9) canal somewhere up 5 (1.3) 6 Dulga' area past Dulgate 7 (1.3) 8 we set up 9 and we'd we'd been fishing for about two and half hours 10 it's aba- about six thirty in the morning 11 this old farmer comes up 12 says er (1.1) Aye aye lads, 13 he said er (0.7) I wou' n' bother it 14 —> they >>drained this area of the canal a few months aG(h)O<< Hhh::::, 15 [ hh::: GGAeehh::: he ] he he Allan 16 —> [ huh huh huh huh huh .] Barry 17 S(h)at there watching our floats for hours uhheh heh: 18 I mean luckily you- you know, you'd gone on- with a car 19 so it's a ma'er o' throw'n ev'ryth'n in th' back 20 ['n' j's go:n'] s'm'ere else sort of (ay) (1.5) Alan 21 [ ° ye:: ° ] Barry 22 could've sat there all bleed'n' day! 23 (1.00) 24 °°an’ not known anythin' about it.°° 25 (4.4) Alan 26 aye

(1) [BNC: KBD 1790-1801]

Alan 1 1788 Well it's, it's luck innit? 2—> 1789 <-|-> I don't know. Barry 3—> 1790 <-|-> I don't know <-|-> what's going on. 4—> 1791 I don't <pause> we got about three, three thirty in the morning, both of them 5—> went out to er <pause> canal somewhere up <pause> Dulgate, past Dulgate 6 <pause> we set up and we'd <pause> we'd been fishing for about two and 7 half hours it's aba-- <pause> about six thirty in the morning this old farmer 8 comes up says er <pause> aye, aye lads, he said er <pause> I wouldn't 9 bother it, they drained this area of the canal a few <voice quality: 10 laughing>months ago! 11—> 1792 And we said, oh<end of voice quality>! Alan 12—> 1793 Yeah. <laugh> <-|-> Barry 13 1794 <-|-> <laugh> <-|-> <pause> <voice quality: laughing>Sat there watching our 14 floats for hours<end of voice quality>! 15 1795 I mean Alan 16 1796 Yeah. Barry 17 1797 I mean luckily you, you know, you'd gone on a car, with a car so it's a matter 18—> of throwing everything in the back <-|-> and just going <-|-> Alan 19—> 1798 <-|-> That's it. Barry 20—> 1799 somewhere else so <pause dur="6"> could have sat there all bleeding day! 21 1800 And not have known anything about it. Alan 22 1801 Aye.


123

The changes that have been made to the original transcript are numerous

affecting many layers of discourse and interaction. Not all of them can be mentioned

in detail here. First, overlap annotation in (1) has been corrected in (2), where it is

indicated by square brackets around the overlapped speech; see lines 1–2, 15–16,

and 20–21. Second, laughter, summarily indicated as \laugh[ in (1), is fully

transcribed in (2) with appropriate vowels and laughter pulses in lines 15 and 16

thus indicating what the laughter ‘sounded’ like. Third, the verbal record has seen

many corrections, some large, some small. For example, Barry’s I don’t know

indicated in (1) in line 3 is not supported by the audio data. Only Alan seems to say I

don’ know (without producing a hearable t on the negation) in line 1; however, due

to its occurring in overlap, it cannot be heard with certainty but represents the

transcriber’s ‘best guess’, as indicated by the parentheses. Barry, in the same

overlap, says I remember (with stress on I), certainly an important change over I

don’t know in (1) in that the verb remember is commonly found in story

introductions and thus counts among the turn design features projecting a

storytelling sequence (Rossano 2013). Also, instead of I don’t- in line 4 in (1),

we find I got- in line 3 in (2). Further, backchannels seem to be mis-recorded in (1):

the orthographic transcript features Yeah in line 16 and That’s it in line 19. In the

audio file, the latter does not seem to occur at all, while the former does occur,

however, clearly later in the turn, in low volume and with the diphthong strechted

into a long monophthong e::; see line 21 in (2). Perhaps the most important textual

correction concerns line eleven in (1): there, Alan appears to use constructed dialog

(or direct speech) in And we said, oh in line 11. The audio tape does not evidence

constructed dialog at this point; the BNC’s transcriber may have misheard Alan’s

laughing delivery of S(h)at in line 17 as said oh. Given the centrality of constructed

dialog to storytelling (e.g., Mayes 1990; Ruhlemann 2013), this correction in (2)

must be considered essential.

Fourth, pauses are transcribed rather differently in the two transcripts. While

the number of pauses is not dramatically different—there are ten pauses in (2) as

opposed to nine in (1)—the durational information is dramatically different: not

only are all pauses but the very first one (which is shorter than 0.3 seconds)

measured in seconds up to one decimal but also the 6-second pause recorded in

transcript (1) has disappeared and given way to a mere 1.5 second pause. What

is more, there is a major new pause in line 26 in (2) that was apparently

overlooked in the BNC transcription. This long inter-speaker pause, or gap, is

interactionally significant in that it conveniently signals the storytelling’s

completion.

Fifth, transcript (2) is rich in articulatory detail. To begin with, in (1) Alan is

transcribed as having used Dulgate twice in its full form, whereas in (2) the first

mention of the location in line 3 is shortened to Dulga’. Other such ‘deviant’

pronunciations include don’ in line 2 (instead of don’t) and woul’ in line 13 where

the d is silent. In line 12, the upward arrow : indicates a sharp rise in pitch, whereas

the upper case A in :Aye aye lads marks increased loudness; the colon(s) used in

206 C. Ruhlemann

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Hhh:::: (line 14) and go:n’ (lines 2 and 15) are used to indicate the stretching of the

sound preceding them. Italics in [I do (line 2) and luckily (line 19) are used to

indicate stress. Punctuation, finally, reflects intonational contours, with the period in

lines 1, 2, and 24 indicating a falling tone and the comma in lines 2, 12, and 14

indicating incomplete intonation.

In sum, there will be little doubt that transcript (2) is of much better quality due to

its greater verbal accuracy and its richer phonological, sequential, temporal and

interactional detail. The key question is whether this level of detail defies XML

transposition. In other words: can CA transcripts be turned into XML transcripts

without compromizing dearly held CA principles?

XML Transcript

In the following I discuss a few key excerpts from the XML version of

transcript (2). The annotation scheme used here is the one underlying

XTranscript, an online tool developed in collaboration with Birmingham City

University for automatic conversion of CA transcripts into the XML syntax

(see Section ‘‘Concluding Remarks’’). The full current scheme can be found in

the ‘‘Appendix’’.

The tagging scheme provides for five broad categories, in XML parlance

‘elements’: the\sequence[ element for sequential features including overlap and

latching; the \voice[ element for phonological characteristics including, for

example, volume, stress, pitch, intonation, etc.; the\timing[ element for temporal

aspects including pausing and variation in tempo of delivery; the\laugh[ element

for laughter including within-speech and between-speech laughter; and, finally, the

\comment[element for transcriber comments relating, for example, to hearability

issues and extra-linguistic events. (For video recordings, more elements are required

to capture gaze and gestures).

In the XML excerpts discussed below, two element types found in the BNC’s

XML file are consistently omitted:\s[ elements for sentence-like units as well as

\c[ elements for grammatical punctuation. In the BNC,\w[ elements have ‘c5’,

‘hw’ and ‘PoS’ attributes. In the interest of legibility of the extracts, the ‘hw’ and

the ‘PoS’ attributes have been removed. Other omissions (for expository reasons)

are indicated by XML comments in the form of\!– –[. Finally, I also show some

rather simple XPath queries for retrieving data from the transcript. The aim here is

to illustrate the potential of this technology for extracting data from densely

annotated XML documents.

Excerpt (3) represents lines 1–2 of the CA transcript in (2). The excerpt illustrates

XML annotation of overlap as a sequential feature, transcriber’s comments, and

some phonological features:


123

(3) 1 2  3 —> <sequence type="overlap" n="1" part="1"> 4 —> <comment hearing="possible"> 5 <w c5="PNP" >I </w> 6 <w c5="VDB" >do</w> 7 —> <voice realization="n' "> 8 —> <w c5="XX0" >n't </w> 9 </voice> 10 <w c5="VVI" >know</w> 11 —> <voice intonation="continued"/> 12 </comment> 13 </sequence> 14 15 16 —> <sequence type="overlap" n="1" part="2"> 17 —> <voice stress="i" degree="much"> 18 <w c5="PNP" > I</w> 19 </voice> 20 <w c5="VVB" >remember</w> 21 <w c5="ADV">once</w> 22 —> <voice stretch="o" degree="much" realization="go:n' "> 23 —> <w c5="VVG"> going</w> 24 </voice> 25 <w> on</w> 26 —> <voice intonation="continued"/> 27 </sequence> 28  29 

In lines 3 and 17, we find \sequence[ elements specified as overlap by

type = ‘‘overlap’’. The\sequence[ elements are wrapped around the simultane-

ously produced verbal material. The elements have two more attributes intended to

provide handles by which to ‘yank out’ different types or components of overlap.

One is ‘n’: its values 1, 2, etc. number the overlaps consecutively in their textual

environment. With this attribute, overlap elements can be extracted by position in

overlap sequences: the first overlap, the second, and so forth. If extracted via ‘n’, the

query will return both the overlapped speech and the overlapping speech. For

example,

//sequence[@type="overlap" and @n="1"]

selects all text-initial overlaps (in the present case, just one).

If, by contrast, the aim is to tease apart overlapping and overlapped speech, the

third attribute ‘part’ comes handy: by specifiying part = ‘‘1’’, only overlapped

speech will be addressed (in most cases, address forms, laughter, tags, or, as in the

present case, turn increments; cf. Jefferson 1979), while specifiying part = ‘‘2’’

addresses overlapping speech. For example:

//sequence[@type="overlap" and @part = "2"]

Within the overlapped sequence, we find in line 4 a\comment[ element. This

element type accomodates the transcriber’s comments related, for example, to extra-

linguistic events or audibility issues; in line 4, the attribute hearing = ‘‘possible’’

indicates that I don’ know is just a candidate hearing. In line 5, we encounter the first

\voice[ element. These elements capture phonological characteristics. The

208 C. Ruhlemann

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\voice[ element is specified as realization = ‘‘n’ ‘‘ and it encloses the \w[element for n’t in line 9; the meaning of realization = ‘‘n’ ‘‘ is, then, that the

contraction n’t is deviantly realized as n’. Why make this distinction? If n’ were

used instead of n’t in the\w[ element, it would appear as a separate entry in a

frequency list. This would be counterintuitive because n’ is hardly a different word

but rather a different realization of n’t. It should therefore be counted among the

occurrences of n’t. This is made possible by its two-fold record both in the\voice[and the \w[ element (frequency lists are commonly made from the latter). We

notice the same double entry for go:n’ as a value on the ‘realization’ attribute in line

22 and going in line 23. The\voice[element in line 22 also contains the attribute

values stretch = ‘‘o’’ indicating the lengthened sound, and degree = ‘‘much’’

indicating the degree of lengthening.

The element in line 11 grasps the intonation contour of Alan’s I don’ know,

which is incomplete or ‘continued’, as indicated by intonation = ‘‘continued’’. This

\voice[element is ‘empty’, meaning that it does not play host to another element

(empty elements have no closing tag, but instead are closed by the forward slash

after the attribute values.) The same phenomenon, continued intonation, is marked-

up in line 26 for Barry’s once go:n’ on. In line 17, two more\voice[attributes are

found: stress = ‘‘i’’, indicating that Barry stresses the personal pronoun, as well as

degree = ‘‘much’’, indicating the degree to which it is stressed.

We instantly see that the XML annotation has already gained considerably in

‘depth’: the\u[ element in line 1 is parent to the\sequence[ element in line 3,

grandparent to the\comment[ element in line 4, grandgrandparent to the first two

\w[ elements in lines 5 and 6 as well as the \voice[ element in line 7, and

grandgrandgrandparent to the \w[ element which is the child of the \phn[element in line 7. Widely-used corpus tools such as WordSmith fail this depth of

annotation; the XPath and XQuery technologies, by contrast, can handle it with

ease. For example, if the interest is in deviant pronunciations occurring in the

overlaps by a distinct speaker, this rather simple XPath returns, in overlaps by

Barry, all\voice[elements that have a ‘realization’ attribute wherever they may be

tucked in the XML hierarchy:

//u[@who="PS03W"]//sequence[@type="overlap"]//voice[@realization]

That CA transcription in XML format need not always cause deep (and

challenging) dependencies is shown in extract (4), which represents lines 12 to 13 in

the CA transcript in (2). The extract illustrates XML annotation for pausing and

variation in delivery:


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(4) 1 2  3 <w c5="VVZ" >says </w> 2 <w c5="UNC" >er </w> 3 —> <timing type="pause" duration="1.1"/> 4 —> <voice pitch="up" > 5 <w c5="ITJ" >Aye</w> 6 </voice> 7 <w c5="ITJ" >aye </w> 8 <w c5="NN2" >lads</w> 9 —> <voice intonation="continued"/> 10 <w c5="PNP" >he </w> 11 <w c5="VVD" >said </w> 12 <w c5="UNC" >er </w> 13 —> <timing type="pause" duration="1.1"/> 14 <w c5="PNP" >I </w> 15 <voice realization="wou' "> 16 <w c5="VM0" >would</w> 17 </voice> 18 <w c5="XX0" >n't </w> 19 <w c5="VVI" >bother </w> 20 <w c5="PNP" >it</w> 21  22 

In (4), lines 3 and 13 show \timing[ elements for the type = ‘‘pause’’; the

attribute values duration = ‘‘1.1’’ and duration = ‘‘0.7’’ record their length. Note that

the pause in line 3 nicely demarcates the beginning of constructed dialog in lines 5–8

thus assuming a function as auditory quotation marker (cf. Bolden 2004; Ruhlemann

2013). Moreover, the attribute pitch = ‘‘up’’ in line 4 accounts for the sharp pitch rise

occurring in Aye. Sharp pitch rises are presumably always interactionally and

discoursally significant. This is demonstrably so in the present case. In (4), the

significance lies in the initiation of constructed dialog, where the pitch rise, much like

the pause, helps the listener identify, to use Goffman’s (1981) terminology, the

speaker’s switch from ‘author’ (of his own words) to ‘animator’ (of the old farmer’s

words). XPath can easily target these pitch rises, simply by addressing the\voice[elements that satisfy the condition of having the attribute value pitch = ‘‘up’’:

//voice[@pitch = "up"]

To investigate whether sharp pitch rises collocate with quotative verbs and,

hence, with constructed dialog (to my knowledge, an as-yet neglected research

question), this query retrieves all words preceding the rise in a 3-word window:

//voice[@pitch = "up"]/preceding::w[position() = 1 to 3]

Excerpt (5) illustrates how the tagging scheme handles laughter. As is well

known, laughter is a central concern in CA research (for example, Jefferson 1985).

CA transcription of laughter aims to approximate what the laughter sounds like by

paying attention to laughter pulses and the appropriate vowel (Liddicott 2007: 26).

Laughter in conversation can occur in two forms: either as within-speech laughter or

as between-speech laughter. The current tagging scheme targets laughter using the

\laugh[ element. The two types of laughter are distinguished by the attributes

‘within-speech’ and ‘between-speech’ respectively. The laughter in lines 13–14 is

210 C. Ruhlemann

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within-speech. Two attribute values are used to grasp it in more detail: the attribute

value form = ‘‘aG(h)O’’ describes the way in which it intrudes the word within

which it occurred while volume = ‘‘high’’ denotes its high intensity on the second

syllable. The two remaining laughter occurrences in lines 17–18, 20, and 23–25

represent between-speech laughter most of it produced by Barry and Alan

simultaneously. The ‘form’ attribute records laughter pulses as well as vowels.

The two laughs’ almost complete occurrence in overlap represents a neat illustration

of Schegloff’s analysis of laughter as a ‘choral’ activity ‘‘NOT to be done serially

(…) but simultaneously’’ (Schegloff 2000, p. 6).

(5) 1 2  3 <w c5="PNP" >they </w> 4 <w c5="VVD" >drained </w> 5 <w c5="DT0" >this </w> 6 <w c5="NN1" >area </w> 7 <w c5="PRF" >of </w> 8 <w c5="AT0" >the </w> 9 <w c5="NN1" >canal </w> 10 <w c5="AT0" >a </w> 11 <w c5="DT0" >few </w> 12 <w c5="NN2" >months </w>

13 —> <laugh type="within-speech" form="aG(h)O" volume="high" > 14 —> <w c5="AV0" >ago</w> 15 </laugh> 16 <sequence type="overlap" n="2" part="1">

17 —> <laugh type="between-speech" form="hh::: GGAeehh::: he" 18 —> volume="high"/> 19 </sequence> 20 —> <laugh type="between-speech" form="he he" > 21 22 23 <voice intonation="fall"> 23 —> <sequence type="overlap" n="2" part="2"> 24 —> <laugh type="between-speech" 25 —> form="huh huh huh huh huh" /> 26 </sequence> 27 </voice> 28 

Finally, the larger storytelling context suggests that the choral laughter does not

occur randomly in the storytelling sequence but in response to the point of the

storytelling, its climax, and its association with the climax is clearly evidenced by

the laughter’s loudness and extended duration (as indicated by the high number of

laughter pulses). I’ll return to this point in the case study in Section ‘‘Case Study:

Backchannels and Overlap in Storytelling Interaction’’.

Needless to say that the XML format makes these instances of laughter, just as

any other nodes in the XML network, readily available for extraction and

examination. A simple XPath query to retrieve laughter occurring in overlap is this:

//sequence[@type = "overlap"]//laugh

This query returns all instances of simultaneous laughter with all the meta-data

included in the elements. If the laughter sound, captured by the ‘form’ attribute, is of

primary interest, call the string() function:

//sequence[@type = "overlap"]//laugh[@description]/string(@description)


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In the following section the aim is to demonstrate the benefits of doing research

on re-transcriptions of the BNC’s audio files. To this end I present a case study on

backchannels occurring in overlap in storytelling interaction.

Case Study: Backchannels and Overlap in Storytelling Interaction

Introduction

Backchannels are unobtrusive vocalisations that are phonologically and semanti-

cally minimal and by which listeners put on record their listening and understand-

ing, and their willingness to continue listening. They are ‘‘not construed as full

turns, but rather pass up the opportunity to take a turn’’ (Levinson and Torreira

2015: 8). There is no shortage of research on backchannels occurring in general

conversation. They have been studied in a wide range of linguistic and linguistics-

related subdisciplines, including conversation analysis (e.g., Jefferson 1986),

sociolinguistics (e.g., Holmes and Stubbe 1997), variational pragmatics (e.g.,

O’Keeffe and Adolphs 2008), and corpus linguistics (e.g., Kjellmer 2009).

Backchannels in storytelling have seen much less scrutiny (e.g., Stivers 2008;

Tolins and Fox Tree 2014; Ruhlemann 2013) and their functions in that particular

(and central) context have not yet been fully understood. The overarching goal in

this brief case study is to contribute to a fuller understanding.

The literature on forms and functions of backchannels is large and diverse.

Kjellmer (2009: 83) notes that it is ‘‘hardly possible to give a finite list of English

backchannels.’’ There is some agreement though as to how backchannels function in

turntaking, namely as non-turn-claiming talk, that is, as talk ‘‘in the back channel,

over which the person who has the turn receives short messages such as yes and uh-

huh without relinquishing the turn’’ (Yngve 1970: 568; see also Wong and Peters

2007: 485; Levinson and Torreira 2015; for a critical discussion of their supposed

‘non-turnhood’, see Ruhlemann 2013, Ruhlemann and Gries 2015). Another

defining feature of backchannels is their ability to occur in overlap, that is,

simultaneously with the main speaker’s talk without being perceived as interrupting

talk (e.g., McCarthy 2003; Wong and Peters 2007). A distinction is sometimes made

between minimal and non-minimal response tokens (e.g., McCarthy 2003) with the

former referring to ‘‘nonword vocalizations such as ‘hnh’ and ‘hmm’’’ (McCarthy

2003: 38) and the latter referring to (strings of) items more readily identifiable as

proper words. Generally, backchannels have been viewed as serving a number of

functions (cf. Stenstrom 1987; Holmes and Stubbe 1997; McCarthy 2003; O’Keeffe

and Adolphs 2008). There is some consensus suggesting that the most basic function

underlying all types of backchannels is vocalizing understanding, that is, ‘‘providing

speakers with feedback that tells them something about how they are being

understood, and thus how they might proceed with the talk’’ (Gardner 1998: 220).

Over and above this basic function additional functions have been distinguished

acting alongside the basic vocalizing understanding function. These additional

functions include the following: the function as ‘continuer’, exhibiting ‘‘an

understanding that an extended unit of talk is underway by another [speaker] and

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that it is not yet, or may not yet be (…) complete’’ (Schegloff 1982: 81). Stivers

(2013) refers to this type of backchannel function as ‘acknowledgement token’

noting that it occurs in the early stages of storytelling interaction. Further, O’Keefe

and Adolphs (2008) identify a distinct function as ‘convergence token’. This

function is ‘‘found at points of convergence in conversations, that is, where

participants agree, or simply converge on opinions or mundane topics’’ (O’Keeffe

and Adolphs 2008: 85). Holmes and Stubbe (1997) describe the function as

‘supportive minimal response’ (Holmes and Stubbe 1997: 11) signaling ‘‘an

increasing degree of interactional involvement on the part of the listener’’; cf.

O’Keefe and Adolphs’s (2008) related notion of ‘engagement tokens’. In Stiver’s

terminology, both convergence tokens and supportive response tokens would

probably be covered by the term ‘affiliative token’. Backchannels serving to register

the recipient’s affiliation with the main speaker, Stivers (2013) adds, are found at

later stages in storytelling interaction, specifically around the climax where the

recipient’s aggreeing and affiliating with the narrator’s ‘stance’ towards the

recounted events becomes relevant. Finally, investigating the temporal properties of

minimal backchannels, Peters and Wong (2015) observe a significant correlation of

the lengths of the listener-controled interval preceding the backchannel yeah and the

lengths of the speaker-controled interval following yeah. They interpret this finding

as accommodation on the part of the speaker, whose ‘‘response time is intended to

match up with that of the listener-produced interval’’ (Peters and Wong 2015: 424)

before yeah. In this case, then, yeah assumes the function of a ‘discontinuer’,

signaling the half-time of the current speaker’s turn and the listener’s wish to take

over the speaking turn after the remaining interval.

Research on overlap, on the other hand, has been one of the mainstays of

conversation-analytic research whereas there has been ‘‘a lack of detailed statistical

analysis of overlaps in corpora’’ (Levinson and Torreira 2015: 6). The long research

tradition in CA arguably originates in Sacks et al. (1974) seminal treatment on turn-

taking, where it was observed that ‘‘[t]ransitions (from one turn to a next) with no

gap and no overlap between them are common’’ (Sacks et al. 1974: 700). ‘No

overlap’, however, is not to be taken as an exact acoustic quantification. As Heldner

and Edlund (2010) have shown, turn transitions from one speaker to another are

most commonly 200 ms long, that is, they occur after a slight gap, while the second

most common type of transition is in overlap, with overlap accounting for 40% of

all transitions (Heldner and Edlund 2010: 564). Cases of zero gap and zero overlap,

by contrast, represented only a ‘‘marginal part’’ in Heldner and Edlund’s (2010:

564) data. Sacks et al.’s (1974) model of turn-taking suggested ‘‘systematic bases

for the occurrence of overlap’’ (Sacks et al. 1974: 706), including competition

between self-selecting speakers (two speakers happen to start up a turn at the same

time) as well as projection (prediction) of turn completion. This latter type has been

found to be ‘‘massively present’’ (Jefferson 1986: 158; cf. also Jefferson

1973, 1986), particularly in cases of ‘terminal overlap’ occurring when a recipient

‘‘reasonably, warrentedly treats some current utterance as complete, ‘transition

ready’’’ (Jefferson 1986: 154) and starts up speaking at the same time as the speaker

adds an increment to an otherwise complete turn (another optional adverbial,

vocative, tag question, etc.).


123

Also, overlap is intimately associated with backchannelling. Indeed, backchan-

nels provide the core ecological niche for the occurrence of overlap in that, for

example in Levinson and Torreira’s (2015) large-scale corpus analysis, ‘‘the

majority of overlap cases (73%) involved a backchannel’’ (Levinson and Torreira

2015: 8).

This brief case study focuses on backchannels in overlap. Its target is thus a

restricted type of speaker transition. While backchannels in conversation generally

may be one context among a number of contexts for overlap (but even there,

backchannels are the prime context of occurrence for overlap; see above), in

storytelling, however, backchannels may be considered crucial. It can be argued that

in storytelling the backchannel response represents the default type of reponse.

Stories, Sacks noted, ‘‘take more than an utterance to produce’’ (Sacks 1992: 222)

and story recipients ‘‘are specifically invited’’ (Sacks 1992: 227) to provide tokens

of listenership to convey ‘‘the recognition that a story is being told’’ (Sacks 1992:

227), a recognition typically accomplished by means of Schegloff’s ‘continuers’.

Similarly, backchannels are used by recipients to affiliate with the teller’s stance

around the climax, an affiliation typically realized by Stivers’s affiliation tokens.

The relationship between backchannels and overlap in storytelling is, then, anything

but peripheral but central to the social practices of action driving the ‘machinery’

(Sacks 1984) of storytelling as an interactional achievement by the teller and the

recipient(s).

The analysis will address five research questions:

(1) How often do backchannels occur in overlap in storytelling?

(2) Which backchannels occur in, which outside of overlap?

(3) How long are backchannel overlaps?

(4) How long are backchannel overlaps compared to the overlapped turn?

(5) How much running speech is occupied by backchannel overlap in

storytelling?

In the following section, I briefly describe the data and methods used for this case

study.

Data and Methods

The data underlying this case study come from the the Narrative Corpus (NC;

Ruhlemann and O’Donnell 2012), extracted from the demographically-sampled

sub-corpus of the British National Corpus (BNC). The NC is a densely annotated

XML corpus. Not only does the NC feature all the corpus-linguistic annotation of its

mother corpus, the BNC, including meta-data on speakers, textual divisions, turn

beginnings, and, characteristically, Parts-of-Speech (PoS) markup. The NC also

integrates an extensive discourse-analytic layer of annotation to capture storytelling-

specific characteristics, including narrative sub-genre (1st person experience, 3rd

person experience, etc.), textual component (pre-narrative component, story-initial

utterance, etc.), textual embedding (free-standing story, or 1st, 2nd, 3rd in story

round), quotatives (SAY, GO, THINK, BE like, etc.), and discourse presentation

214 C. Ruhlemann

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(direct speech, free direct speech, indirect speech, etc.). Yet another discourse-

analytic annotation type, which is critical in the present connection, is

participant role. This annotation category distinguishes narrator and recipient

sub-roles on an utterance-by-utterance basis. One of the two recipient sub-roles

recognized in the NC is ‘responsive recipiency’, tagged as PRR. Tagging a

response as PRR is based, not on a formal, but a functional definition of

backchannel response: utterances by story recipients are tagged PRR if, and only

if, line-by-line analysis of the sequential context suggests that the recipient’s

utterance is ‘informationally redundant’ [cf. Walker’s (1993) concept of

informationally-redundant utterances] in that the recipient does not add, or

elicit via questions, any topic-related content to the story, but merely signals

distinct levels of reception (acknowledging or affiliating) and structural analysis

of the on-going telling performance. Thus, responses labeled PRR represent

backchannel responses.

The NC is not based on the BNC’s audio data. For the purpose of the case study,

a targeted re-analysis of the NC data was undertaken where audio files were

available. The steps involved in this process were the following.

First, using complex XQuery scripts, all backchannel responses and the

storytelling turns to which they were a response were extracted from the NC;

also extracted were the number of words in each storytelling turn as well as the

positions of the backchannels in the sequential context of their storytelling.

Following established procedure (e.g., Hoey and O’Donnell 2008; Ruhlemann

2013), positions were calculated as proportions obtained from dividing the number

of words preceding the backchannel by the total number of words in the story.

Positional values range on a continuum from 0 (very first position) to 1 (very last

position). Thus, for example, an mm by a recipient occurring after a story-initial turn

of, say, 20 words’ length and occurring in a story that turns out to be a 100 words

long would be positioned at 9/100 = 0.09. The total number of backchannel

responses thus extracted was 1,265.

Second, all turn-backchannel pairs were tested for whether they were available in

the BNC audio files. The pairs available in audio were re-transcribed and re-

analyzed using Audacity (http://www.audacityteam.org/), an acoustic analysis

software. Re-transcription involved correcting errors in the BNC’s orthographic

transcript, while re-analysis involved re-measuring intra- and inter-speaker pauses,

measuring durations of overlaps, defining the exact extent of overlap in the text (for

example, if a word was only partially overlapped). The number of turn-backchannel

pairs thus processed was 820 occurring in 231 storytellings; the backchannels were

produced by 189 distinct speakers covering a wide range of socio-demographics.

Third, the data were examined using descriptive and analytic statistics in R, a

programming language and environment for data analysis and graphical represen-

tation (cf., for example, Gries 2009a, b).


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http://www.audacityteam.org/

Results

The first research question (how often do backchannels occur in overlap in

storytelling?) can be addressed by comparing frequencies of backchannels in

overlap and outside of overlap.

As shown in Table 1, backchannel overlap was found for 336 backchannels,

accounting for 41% of all backchannels, whereas 478 backchannels occurred

without overlap, representing 58%. This distribution is largely consistent with

reported distributions. Kjellmer (2009: 86), not specifying proportions, notes that

backchannels occur ‘‘predominantly turn-externally’’ (i.e., outside of overlap). ten

Bosch et al. (2005) found overlap to account for 44% of speaker changes in face-to-

face conversation. Heldner and Edlund (2010) report 40% of speaker-transitions

involving overlaps. Only in Levinson and Torreira (2015) the proportion of overlap

was clearly lower, with 30% of transitions occurring in overlap. The present

findings relate only to a subset of overlap transitions, namely backchannel overlap,

not to overlap tout court. Nonetheless, considering most reported proportions, the

proportion of overlap incurred by backchannels in storytelling is a fair reflection of

the proportion of overlap incurred by any linguistic means.

The second question (Which backchannels occur in, which outside of overlap?)

has, to the best of my knowledge, not yet been addressed in published research.

Given that the data are drawn from a representative corpus (the NC; cf.

Section ‘‘Data and Methods’’), the question can be approached with a view to

statictical analysis. To this end, frequency lists were generated for two subsets:

(i) backchannels occurring in overlap, and (ii) backchannels occurring outside of

overlap. All intersecting backchannels (i.e., backchannels included in both subsets)

were identified; items not intersecting the subsets were highly infrequent and not

further investigated. Permutation tests were performed on the intersecting items.

The permutation test is a computationally expensive, non-parametric test. Under-

lying it is the assumption that, given the null hypothesis that labelings (such as, in

the present case, whether a backchannel occurs in overlap or not) are arbitrary, the

significance of a given distribution (in the present case, the frequency of

backchannels in and out of overlap) can be assessed ‘‘via comparison with the

distribution of values obtained when the labels are permuted’’ (Nichols and Holmes

2001: 2–3). The results are displayed in Table 2.

As can be seen from Table 2, the test returned significant results for 6 items: mm,

mhm, no, and yeah are more frequent outside of overlap, while yeah yeah as well as

laughter are more common in overlap. As regards laughter, it will not be surprising

that it is more frequently overlapped than free-standing: this tendency has already

Table 1 Frequencies and

percentages of BC in and out of

overlap

BC Frequency %

Overlap 336 40.98

No overlap 478 58.29

Unclear 6 0.73

Total 820 100

216 C. Ruhlemann

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been observed in CA research. Schegloff, for example, views laughter as a ‘choral’

activity ‘‘NOT to be done serially (…) but simultaneously’’ (Schegloff 2000: 6). But

the significant results also invite interesting hypotheses with regard to the sequential

placement of overlap in storytelling. Stivers (2013: 201) views backchannels such as

mm and mhm as acknowledging tokens occurring ‘‘early in the telling’’; by contrast,

affiliative items ‘‘are common (…) near the high point of the telling’’ (Stivers 2013:

201). She cites head nods, great!, and wow! as examples of affiliative backchannels.

Laughter, it seems, in most cases also performs an affiliative rather than an

acknowledging function. Thus, it is tempting to hypothesize that overlapped

laughter is more commonly found around the climax. To test this hypothesis, based

on the procedure described in Section ‘‘Data and Methods’’, the positions of

overlapped laughter in the context of their storytellings (and, for comparison, of mm

and yeah) were calculated. The results are depicted in the three histograms in Fig. 1.

Table 2 Frequencies and permutation test statistics for backchannels in and out of overlap

BC Freq_in Freq_out CI_lower CI_upper p-value Sign

((laughs)) 54 49 0.01042 0.01042 0.01815 *

(unclear) 4 1 -0.00397 -0.00395 0.16576 ns

ah 3 4 -0.01087 -0.01083 1 ns

aha 3 8 -0.025 -0.025 0.37962 ns

aye 1 4 -0.01571 0.00513 0.4111 ns

did she 1 1 -0.00529 0.00532 1 ns

hm 2 6 -0.02062 0.00526 0.48112 ns

i know 1 2 -0.01031 0.00526 1 ns

i see 1 1 -0.00529 0.00532 1 ns

it was 1 1 -0.00529 0.00532 1 ns

mhm 4 24 -0.06404 -0.01471 0.00286 **

mm 56 112 -0.12435 -0.01042 0.02204 *

mm mm 9 6 -0.0051 0.03185 0.18516 ns

no 1 12 -0.0402 -0.00508 0.01918 *

oh 12 20 -0.03415 0.02051 0.71708 ns

oh (unclear) 1 1 -0.00529 0.00532 1 ns

oh no 3 2 -0.00529 0.01531 0.65393 ns

oh right 1 1 -0.00529 0.00532 1 ns

oh yes 1 3 -0.01463 -0.0146 0.64666 ns

ooh 3 1 -0.0049 0.01554 0.31196 ns

that’s right 1 3 -0.01463 -0.0146 0.64666 ns

tt 1 1 -0.00529 0.00532 1 ns

yeah 52 109 -0.12889 -0.12887 0.01218 *

yeah yeah 10 3 0.0051 0.04054 0.01053 *

yes 12 15 -0.02066 -0.02062 0.84289 ns

yes yeah 1 2 -0.01031 0.00526 1 ns


123

Story climaxes are hard to predict positionally; they can only be ascertained

sequentially through line-by-line analysis of how teller and recipient(s) jointly

organize the telling into components (cf. Goodwin 1984). However, we can

reasonably assume that climaxes are unlikely to occur early but likely to occur late

in the story sequence. So under the hypothesis that laughter has a special

relationship with the climax we will expect to find higher frequencies of occurrence

in later positions. This means, we will expect frequencies to peak towards the value

1 rather than the value 0 of the positional continuum.

As regards mm and yeah, we do see in Fig. 1 an uptick in frequency of

occurrence (depicted in the ‘bins’) or probability of frequency of occurrence

(depicted in the density curve) in the final interval in the storytelling sequence.

However, their distributions as a whole are bimodal; that is, the density curves form

a second, and even bigger, hump in earlier intervals suggesting that the probability

of getting larger frequencies in earlier positions is even higher than in the last

interval. The density curve for laughter, by contrast, rises constantly as the

storytelling progresses from its inception until it reaches its (high) peak at the

boundary between the ninth and the tenth interval, the place where the story climax

is most likely to occur. This is evidence to suggest that overlapping laughter indeed

Fig. 1 Histograms of positions of laughter, mm, and yeah occurring in overlap in the context of theirstories

218 C. Ruhlemann

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clusters around positions in the storytelling sequence where the storyteller is likely

to reach the high point of the story. Consider, for illustration, the storytelling

‘‘Dropped your johnnies’’ in (6). While there is laughter already early on (see lines

3, 5, and 6–7), the overlapping laughter that is the most extended and the one with

the greatest acoustic intensity, in lines 22–24, represents not only Joanne’s and

Helen’s acceptance of Andy’s invitation to laugh with him (cf. Jefferson 1979) but

also a clearly affiliative reponse by the two recipients to the story climax realized, as

so often (e.g., Mayes 1990, Holt 1996), in constructed dialog (hi Greg (1.57)

dropped your johnnies>) in line 20.

(6) [“Dropped your johnnies”, BNC: KCE 2659-2674] Andy 1 Oh it was so funny at work today, 2 Greg fell off his chair. Helena 3 Hh. Andy 4 Packet of condoms fell out of his pocket Helena 5 eh Hih Heh Huh huh huh huh Andy 6 And they were ripped [ h'::: ] Helena 7 [ heh Heh eh heh ] Andy 8 Ah no he was, 9 he, he wouldn't sit on his chair 10 cos he'd just called me an arsehole? (0.45) 11 and I goes oh sit down on y’ chair Greg mh'm 12 I said sit down Gregory and shut up, 13 so he went to sit down 14 but his chair weren't there? (0.89)

15 All I saw were his pair of legs ( ) over the desk 16 and him goin’ AAAAgh::Joanne 17 °And his [condoms ( )] Andy 18 [ He got up ] 19 and then one of the girls says (.) 20 hi Greg (1.57) dropped your johnnies¿ 22 [ heh heh ] Joanne 23 [ heh hih hih heh hih heh hih huh huh] Helen 24 [ h'm Heh Heh Heh heh heh heh] Andy 25 [ I’ve ] never seen anyone go so red in my life.

This is evidence, then, to suggest that laughter in overlap has its preferred locus

around the climax. It is, then, synchronized with story organization. This is no small

discovery in that it adds weight to Goodwin’s observation that recipients attend to

the task ‘‘not simply of listening to the events being recounted but rather of

distinguishing different subcomponents of the talk in terms of the alternative

possibilities for action they invoke’’ (Goodwin 1984: 243).

The third question concerns the durations of backchannel overlaps. As noted,

using Audacity the temporal extensions of overlaps were measured. In a number of

cases, durations could not be established with certainty, due to poor audio quality or

interfering background noises. The number of overlaps (and their corresponding

turns) for which durations could be reliably observed was 291.

As depicted in the scatterplot in the left panel of Fig. 2, overlaps are brief: the

median duration is half a second, while the mean is 0.63 seconds. Only occasionally

did overlap exceed one second. This finding is almost perfectly consistent with

previous research. Heldner and Edlund (2010), for example, report for overlap a

mean length of 610 ms and a median length of 470 ms. The findings then support the

view that ‘‘the bulk of overlaps are of short duration’’ (Levinson and Torreira 2015:

4; cf. also Wong and Peters 2007; Peters and Wong 2015).


123

The stripchart in the right panel in Fig. 2 also depicts overlap durations but

additionally presents color codings for the five most frequent backchannels occurring

in overlap: mm (51 occurrences), yeah (46 occ.), laughter (40 occ.), oh (11 occ.), and

yes (11 occ.). It can be seen that the median durations (depicted in the dotted vertical

lines) for mm, yeah, oh, and yes are all smaller than the median for all backchannels in

overlap (which is 0.5). Conversely, the median duration for laughter is far more

extended with 0.7 seconds. This stark difference is arguably due not only to the serial

extensibility of the number of laughter pulses (he, hu, etc.) but also to the fact that

laughter, as a non-verbal vocalization, is ‘‘not turn organized’’ (Lerner 1996: 259) and

will therefore not be construed as an attempt to take the floor even if extended.

The fourth question is intimately related to the third: how long are the overlapping

backchannels relative to the length of the turn to which the backchannel is a response?

This is best answered by calculating the proportions of overlap durations. As can be

seen from the histogram in Fig. 3, by far the overwhelming majority of overlaps

occupy less than 20% of the turn to which they respond. The median proportion is

0.122 (12%) and the mean proportion is 0.185 (19%). Considering the brevity of

overlaps discussed above, the low proportions will not be surprising.

The next question addressed also concerns proportions of overlap but casts the net

wider focusing on the amount of running speech occupied by backchannel overlap, not

only in the ovelapped turn, but in storytelling. This is an intriguing question but

answering it is anything but straightforward. A successful attempt to address the

question conclusively would require the availability of not only a representative corpus

of storytellings (a condition arguably satisfied in the case of the NC) but also of

exhaustive measurements of all turns as well as all backchannel responses in and out of

overlap in that corpus. What we do have are exhaustive measurements of backchannel

overlaps and measurements of the turns to which they respond. Measuring the

durations of non-overlapped turns was far beyond the resources available for this case

Fig. 2 Left panel scatterplot of overlap durations; right panel stripchart of overlap durations withhighlighting of the durations of the top six most frequent backchannels in overlap

220 C. Ruhlemann

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study. However, the measurements available allow us to predict the durations of non-

overlapped turns. As noted in Section ‘‘Data and Methods’’, we also extracted the

number of words in each storyteller turn in the sample. It seems reasonable to assume

that a turn’s verbosity is correlated with the turn’s duration: a one or two-word turn can

be expected to take up less time than a 10- or 20-word turn. This assumption can be

tested based on the turns for which data for both variables (turn verbosity, turn

duration) are available. Indeed, according to a Kendall’s rank correlation test, there

exists a very strong correlation (tau = 0.819023) which is very highly significant

(p\2.2e-16): the more words in a turn, the longer the turn (and the inverse).

The relationship between turn verbosity as the independent (explanatory) variable and

turn duration as the dependent (outcome) variable can be modeled in a linear regression

model, shown in Fig. 4: the data points cluster very closely around the regression line.

Therefore, not surprisingly, the adjusted R-squared value of more than 0.9 is very close

to its maximum value 1, underscoring, again, that the correlation is very powerful.

Based on the actual observations the linear model also computes a ‘slope’

representing the factor by which the duration of a turn increases or decreases depending

on the number of words in it. The slope is key for predicting the durations of turns for

which no durational information is available: the value can be extracted and the number

of words in a turn without observed duration can be multiplied with it. The resulting

product is the turn’s predicted duration. Proceeding in this way, the predicted durations

for all turns without observed duration were calculated. Based on both observed

durations (for turns that were overlapped by backchannels) and predicted durations (for

turns not overlapped by backchannels) the proportion of speech in overlap out of all

running speech in storytelling could be estimated.

Fig. 3 Histogram with density curve for overlap proportions


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The estimate obtained was for overlap to occupy 3.2% of all speech in

storytelling interaction. This proportion is in stark contrast with reported propor-

tions. Following Sacks et al. (1974) observation that ‘‘overwhelmingly, one party

speaks at a time’’ (Sacks et al. 1974: 700), an early overall estimate by Levinson

(1983) predicted that ‘‘less (and often considerably less) than 5 per cent of the

speech stream is delivered in overlap’’. While Levinson (1983) did not provide

statistical evidence to back up the estimate, Levinson and Torreira (2015) did

provide this evidence drawn from large corpora of conversation, finding that overlap

occupies ‘‘less than 5% of the speech stream’’ (Levinson and Torreira 2015: 4).2 In

Norwine and Murphy’s (1938) early study on overlap in telephone dialogs, overlap

accounted for 8% of running speech in telephone conversations!3

So, clearly, our model predicts that the total amount of overlap will be smaller in

storytelling than in conversation generally and much smaller than in telephone

conversation. Can we make linguistic sense of this difference?

Fig. 4 Linear regression model for turn duration as a function of turn verbosity

2 In Levinson and Torreira (2015) the percentage of 5% refers to overlap in turns including all silent

parts, i.e., inter- and intra-speaker pauses; when silent parts are excluded, the percentage is 3.8%. This

proportion is clearly closer to the 3.2% obtained from the model. However, the model’s most critical

coefficient, the slope, is calculated on the basis of turn lengths including silent parts. So the percentage of

3.2% is best juxtaposed to the percentage of 5%.3 The proportion of 8% is not stated explicitly but can be read off the cumulative distribution

(summation) of response times shorter than 0 ms (overlaps) on the right hand scale (scale b) of Fig. 5 on

page 289 of Norwine & Murphy’s study; I’m indebted to Mattias Heldner (personal email

communication) for this pointer.

222 C. Ruhlemann

123

It appears that the difference may be plausibly explained with the greater turn

length typical of storytelling. Turns in storytelling have frequently been observed to

outsize turns in general conversation. Indeed, as Ochs and Capps put it, ‘‘[e]xtended

turn length by a principal teller is a distinguishing feature of personal narratives’’

(Ochs and Capps 2001: 37; see also Ruhlemann 2013: Chapter 3).4 However,

extended turn length alone would not help explain the low percentage of overlap in

storytelling if overlap length were correlated with turn length, i.e., if overlaps

became longer as the turn gets longer, and became shorter as the turn gets shorter.

According to a Kendall’s correlation test, there is a highly significant correlation (p

\0.001); however, the coefficient of 0.1735229 obtained from the test suggests that

the correlation is very weak. Overlap durations and turn durations can, thus, be

considered rather unrelated, as illustrated by the dotchart in Fig. 5.

The observed turn lengths in the dotchart in Fig. 5 are sorted in descending order

with the respective overlap duration plotted over them. For example, the longest

turn in the sample, with a duration of 68.32 seconds, is depicted in the uppermost

grey line; the respective backchannel-incurred overlap is just 1.45 seconds long. It

can be seen that, while turns can occasionally be just slightly longer than the

overlapped backchannel (see the lower third of the dotchart), more frequently they

will outsize overlap considerably (see the upper two thirds of the chart). What is

more, as turns grow in length the overlaps do not noticeably grow with them. This

Fig. 5 Dotchart comparing observed durations of turns (grey) and observed durations of overlap (blue).(Color figure online)

4 Another contributing factor to greater turn length in storytelling is the significantly greater number of

storyteller pauses within storytelling turns (cf. Ruhlemann 2013).


123

relative asynchrony, clearly visible to the naked eye in the dotchart, is also clearly

indicated by a statistical measure, the standard deviation, ‘‘a measure of how closely

the data cluster around the mean’’ (Woods et al. 1986: 41): the sd for turn length

(8.03) is almost 19 times the sd for overlap length (0.43). Thus, the influence of

extended turn length on overlap length can be considered negligible. On the whole,

overlaps remain brief whatever the turn’s size. Because turn length and overlap length

are, then, out of ‘sync’, it is plausible to assume that it is the greater length of story

turns that drives up the proportion of speech ‘in the clear’ and reduces the proportion

of overlapped speech in storytelling compared to general conversation and telephone

conversation. Consider (7) for illustration: Susan, a grown-up woman, is telling how

her mother, visiting her in her house, behaved as if Susan was still a little child:

(7) ["Mothers", BNC: KBG 303-334]

Susan: 13 °°Yeah°° Cos mum made me laugh tonight (.) 14 cos she came in and (.) 15 she said oh you 16 can have a hot cross bun the:re, I said well (.) 17 I think I'll have a piece of chocolate cake 18 and she said (1.0) alright 19 I said, oh I'm gla(h)d you approve

20 being as though it's mi:ne and it's m(h)y21 house, I c(h)an ea(h)t what I wa(hhh)nt 22 A(h)nd she'd just totally forgotten

23 [ .hh uhm uh::: ]=Carl: 24 [Mm, comfortably so]Susan: 25 =it was really quite funny .hh

Susan’s first turn is quite extended, containing 71 words as well as four pauses (of

which three are below 0.25 seconds and one is one second long). The storytelling as

a whole is 20.34 seconds long, the overlap occurring in lines 23 and 24 between

Susan’s part inhalation .hh, part vocalized hesitation uhm uh::: and Carl’s

assessment Mm, comfortably so occupies 0.61 seconds, accounting for only 3.0%

of the total speech stream in the story.

Extended turn size may, then, cause a significantly lower overlap proportion in

storytelling compared to conversation generally and telephone conversation

specifically. However, two caveats need to be borne in mind. First, backchannels

are not the only loci where overlap occurs (cf. Section ‘‘Introduction’’). The present

analysis was based exclusively on overlap incurred by backchannels. Obviously,

alternative types of overlap will have to be ‘counted in’ in more comprehensive

analyses. Also, the present analysis is based not only on observed durations but also

to a large extent on predicted durations derived from a linear model. The proportion

of 3.2% discovered here thus represents an estimate. The estimate’s accuracy, or

lack thereof, needs yet to be ascertained in future research.

Concluding Remarks

The excerpts and analyses discussed above demonstrate the research opportunity

offered by the availability of the audio files of the BNC. The audio files open up

avenues for research that have thus far been blocked.

First, while many corpora with merely orthographic transcription have been

advertized as presenting ‘real speech’ by ‘real speakers’, the BNC’s audio files put

real voices to these speakers. The hearability is a game changer. Rather than relying

224 C. Ruhlemann

123

on (faulty) orthographic transcripts ‘deaf’ to meaningful nuances of actual delivery,

we, as analysts, are permitted access to the richness of phonology in interaction.

Second, the amount of speech available for acoustic analysis is impressive. Albert

(private email communication) estimates that the audio files for the conversational (i.e.,

the ‘demographically-sampled’) subcorpus of the BNC record ‘‘about 164 hours of

audio’’ produced by more than 600 distinct speakers that are likely to ‘represent’ the

population of British speakers because the samples were drawn representatively

Ruhlemann from a very wide-ranging socio-demographic spread (Crowdy 1995: 225).

The sample size and its representativeness Ruhlemann are crucial for quantification,

especially for statistical inference. For statistical tests assume samples that are sizable

and random or at least representative Ruhlemann. By contrast, as Robinson notes, ‘‘[a]s

is the case with much social-scientific research utilizing college-student populations,

most CA data are nonrandom, convenience samples. In such cases, statistical results are

crude rules of thumb’’ (Robinson 2007: 72). The audio data available via BNCweb

allows quantification to get beyond crude rules of thumb enabling reliable statistical

testing and hence generalization: regularities, or practices of social action, found for the

sample can confidently be generalized to the larger population (e.g., of storytelling).

Third, corpus-linguistic transcripts and conversation-analytic transcripts can be

merged and integrated into an XML architecture. The integration into XML pays

dividends on three counts. Firstly, XML is by now the encoding standard for

computerized text world-wide. Working on XML-formatted CA transcripts will, then,

greatly enhance their exportability, storability, and sharability. Secondly, XML

documents are networks in which all nodes are connected in one way or another. This

omni-connectedness is key in that any node or set of nodes in the document can be

addressed and extracted using appropriate XML query tools such as XPath and

XQuery. For CA transcription, XML formatting is a game changer: while CA

researchers have been used to searching their data manually, with all the limitations to

size and extractability of data, XML transcripts allow for efficient automatic retrieval,

extraction, and analysis of very complex and very large data sets. Thus, XML will

also facilitate what has so far been beyond the reach of orthodox CA research:

examining data with a view to large-scale quantification and eventual statistical

evaluation. The third reason why the transposition into XML is worth the effort is the

unique integration of two distinct approaches to conversational data—the corpus-

linguistic one embodied in the exhaustive annotation for word class through PoS

tagging and the conversation-analytic one embodied in the careful attention to situated

interactional detail. While a number of CA transcription tools such as ELAN

(Wittenburg et al. 2006), EXMARaLDA (Schmidt and Worner 2014), and FOLKER

(Schmidt and Schutte 2014) do have an XML component, none of them have to date

any PoS tagging functionality. In other words, an integrated corpus-linguistic and

conversation-analytic transcription in XML format allows high-efficiency access to

conversational data both on the (lower) lexical to grammatical levels as well as the

(higher) discourse and pragmatic levels of interaction—a potentially fruitful marriage

in the spirit of the recent rapprochement of CL and CA witnessed in the burgeoning

field of corpus pragmatics (cf., for example, Aijmer and Ruhlemann 2015).

Where to from here? There are intriguing research projects under way that aim to

integrate CL and CA in XML. First, Albert et al. (2015) created CABNC, a corpus


123

based on the demographically-sampled (conversational) BNC audio files in XML.

While not yet transcribed in CA style, as aimed for, what the corpus does offer already

is additional information in the form of exact durations for both\s[ (sentence-like)

units and\w[ (word) elements. Since durational aspects are critical in interactional

terms and hence central in CA, for example in the study of latching, overlap, or tempo

of delivery, this added data may prove invaluable for future research.

Second, a major step towards integrating CL and CA transcription has been taken

through the recent development at Birmingham City University of XTranscript, an

online tool that converts CA transcripts into XML; it is available at: http://rdues.

bcu.ac.uk/cgi-bin/xtranscript/index.cgi. XTranscript may be immensely useful for

CA researchers, who typically store considerable amounts of CA transcripts in

formats such as MS Word. XTranscript offers an elegant and efficient shortcut from

that (non-machine-readable) format to XML. In its current form, XTranscript works

with the tagging scheme detailed in the ‘‘Appendix’’. The scheme is closely aligned

with Jeffersonian trancription conventions and only little post-editing is necessary to

obtain from a CA transcript rich in interactional detail a fully functional XML

version. Finally, XTranscript offers the additional option to automatically PoS-tag

the transcripts in the XML output (based on the Stanford tagger) thus facilitating

fully integrated CL and CA transcription in XML.

Finally, work is underway to update the Narrative Corpus (NC; Ruhlemann and

O’Donnell 2012) based on the BNC audio files. As noted, the NC is a corpus of

storytellings and their surrounding conversational contexts extracted from the

British National Corpus (BNC). The updated NC, which will be re-named

Storytelling Interaction Corpus (SITCO), will thus integrate three levels of

observation: the corpus-linguistic level with its systematic analysis of morpho-

syntactic function (PoS) of every single lexical item in the corpus, the discourse-

analytic level with detailed attention paid to discourse type, discourse structure,

discourse presentation and discourse roles, and the conversation-analytic level with

its focus on interactionally significant details of delivery of talk and other conduct. It

is hoped that the integration of the three distinct levels of observation will make the

updated NC, or SITCO, unique: such an integration has not been attempted so far

and will potentially help advance the study of storytelling interaction.

To conclude, integrating CL and CA in XML has significant potential. It may

provide CL with the long-sought level of interactionally relevant detail that is

necessary to examine talk, not as an empoverished variant of writing, but as talk in

its full interactional richness and complexity. By facilitating serious quantitative

examination, the integration of CL and CA in XML may not only advance the

methodological scope of CA but also widen CA’s appeal to related areas of

research. As Stivers notes, the combination ‘‘with quantitative methods enables CA

research to play a role in a wider range of research questions and to speak to a

broader audience than would otherwise be possible’’ (Stivers 2015: 2).

Appendix

See Table 3.

226 C. Ruhlemann

123

http://rdues.bcu.ac.uk/cgi-bin/xtranscript/index.cgi

http://rdues.bcu.ac.uk/cgi-bin/xtranscript/index.cgi

Table

3XTranscripttaggingschem

e

Category

XML

elem

ent

Sub-

category

XMLattributes&

attribute

values

CA

symbol

Description

Sequential

aspects

\sequence[

overlap:

\sequence

type=

"overlap"[

[]

overlapped/overlappingspeech

\sequence

n=

""[

idnumber

ofoverlap

\sequence

part=

"1/2/…

"[positionoftheoverlapin

asequence

of

overlaps

\sequence

from

=""orto

=""[

overlapin

mid-w

ord

latching:

\sequence

type=

"latching"[

5oneturn

latched

onto

nextturn

with

less-than-usual

ornogap

atall

\sequence

position=

"start"or"end"or"w

ithin"[

thepositionwithin

theturn

ofthelatch

Tem

poral

aspects

\timing[

pauses:

\timingtype=

"pause"duration=

""[

(.)or(1.2)

shortorlonger

pause

speed-up:

\timingspeed=

"faster"

degree=

"much"or"m

ore"

or"m

ost"[

>a<

increase

inspeed

slow-down:

\timingspeed=

"slower"degree=

"much"or"m

ore"

or"m

ost"[

<a>

decreasein

speed

Phonological

aspects

\voice[

intonation:

\voiceintonation=

"rise"[

?question(-like)

rise

\voiceintonation=

"halfrise"[

>or?,

rise

stronger

than

acommabutweaker

than

aquestionmark

\voiceintonation=

"weakrise"[

>weakly

risingintonation

\voiceintonation=

"fall"[

.fallingintonation

\voiceintonation=

"continued"[

,continued

intonation

\voiceintonation=

"level"[

_level

intonation

\voiceintonation=

"anim

ated"[

!anim

ated

tone,

notnecessarily

an

exclam

ation


123

Table

3continued

Category

XML

elem

ent

Sub-

category

XMLattributes&

attribute

values

CA

symbol

Description

pitch change:

\voicepitch

="up"[

:or^

sharprise

inpitch

\voicepitch

="updown"[

:;

sharprisefallin

pitch

\voicepitch

="down"[

;or|

sharpfallin

pitch

volume:

\voicevolume=

"high"[

Aorbold

form

atting

loudvoice

\voicevolume=

"low"degree=

"much"or

degree=

"more"ordegree=

"most"[

�asoftvoice;

threedegrees

stretching:

\voicestretch=

""degree=

"much"or

degree=

"more"ordegree=

"most"word

=""[

a::

lengthened

sound;threedegrees;

stretched

letter

andword

infull

stress:

\voicestress

=""degree=

"much"or"m

ore"or

"most"[

aoraorA

orbold

form

atting

stressed

orheavilystressed

orvery

heavilystressed

sound

realization:

\voicerealization=

""[

deviantrealizationofword

truncation:

\voicetruncation=

""[

-cut-offin

mid-w

ord

aspiration:

\voiceaspiration=

"inhale"

oraspiration=

"exhale"[

.horh.

inhalationorexhalation

\voiceform

="h"or"hh"or"hhh"[

hh

extentofaspiration

smilevoice:

\voicequality=

"smile"[

£talk

producedwhilesm

iling

creaky

voice:

\voicequality=

"creaky"[

*or#

wordspronouncedwithacreak

trem

ulous

voice:

\voicequality=

"tremulous"[

~trem

ulousspeech

Laughter

\laugh[

within-

speech:

\laughtype=

"within-speech"form

=""[

a(h)a

laughingwithin

words

\laughvolume=

"high"orvolume=

"low"[

(H)or(h)

loudorsoftwithin-speech

laughter

228 C. Ruhlemann

123

Table

3continued

Category

XML

elem

ent

Sub-

category

XMLattributes&

attribute

values

CA

symbol

Description

between-

speech:

\laughtype=

"between-speech"form

=""[

h,ha,hah,heh,

hih,hohorhuh

laughingbetweenwords

\laughvolume=

"high"orvolume=

"low"[

Horh

loudorsoftbetween-speech

laughter

Comments

\comment[

onhearing:

\commenthearing=

"unclear"[

()

unclearhearing

\commenthearing=

"possible"[

(a)

possible

hearing

\commenthearing=

"alternative"

alternative=

""[

(a/b)

alternativehearings;specified

in

‘alternative’

attribute

onevent:

\commentevent=

""[

(())

extra-linguisticevent

onanything

else:

\commentother

=""[

other

types

ofcomment

Gaze

\gaze[

direction:

\gazeto

=""duration=

""[

Xinitial1.3

gazed-atparticipant;andduration

\gazeto

="down"duration=

""[

X;1.3

downwardgaze;

duration

\gazeto

="up"duration=

""[

X:1.3

upwardgaze;

duration

\gazeto

="side"

duration=

""[

X/

1.3

orX?

1.3

sidew

aysgazeaw

ayfrom

participant(s);

duration

\gazeto

="shift"

duration=""[

X1.3

shiftinggaze;

duration

Gesture

\gesture[

hand

gesture:

\gesture

type="hand"description=""duration=""[

descriptionanddurationofhandgesture

facial

expression:

\gesture

type="face"

description=""duration=""[

descriptionanddurationoffacial

expression


123

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