the influence of tempo and speaking style on timing patterns in polish agnieszka wagner, katarzyna...

The influence of tempo and speaking style on timing patterns in Polish

Agnieszka Wagner, Katarzyna KlessaInstitute of Linguistics, Adam Mickiewicz University in Poznan, Poland

ExLing 2015 - 6th International Conference on Experimental Linguistics, 26-27 June, Athens, Greece

Background and motivation

• The effect of speech tempo on timing patterns:

▫ well documented for various languages, but not for Polish

▫ significant for the results of the debate on rhythmic classification of languages ( stress/syllable/mora-timing) and correlates of perceived speech rhythm (metrics vs. other models/concepts)

• State of the art:

▫ tendency towards greater syllable-timing/stress-timing with descreasing/increasing tempo (Russo & Barry 2008, Malisz 2013)

▫ higher/lower durational variability (of consonantal and vocalic intervals) at slower/faster rates (Barry et al. 2003, Dellwo 2010)

▫ average spech rate and tempo differentiation are language-specific (Dellwo & Wagner 2003): FR GER EN


Background and motivation• Timing patterns & speaking styles – state of the art:

▫ higher durational variability ( rhythm metrics) and a shift towards stress-timing in spontaneous speech (Arvaniti 2012)

▫ higher nPVI(syl), regression intercept and slope ( TGA – Time Group Analysis) in conversational speech than in read text, and in more informal speaking styles (Yu, Gibbon & Klessa 2014, Gibbon, Klessa & Bachan 2014)

• Goals:

▫ the effects of speech rate and style variation on timing properties of Polish

▫ the relationship between intended and laboratory measured tempo


Methodology• Speech corpus accessed via „timing & duration” database:

▫ 5 speakers

▫ 3 speaking styles poems (different metres)

text

3 x 5 sentences of different phonotactic structure (stressed-timed, syllable-timed and uncontrolled)

▫ 5 intended tempi: very slow to very fast (vslow, slow, norm, fast, vfast)

▫ for future application: 15 non-native speakers (different L1); spontaneous speech, mini-dialogues

• automatic segmentation and labeling at phone, syllable & word level with AnnotationPro (Klessa, Karpinski, Wagner 2013)

▫ verification and manual correction by trained annotators


Methodology

• TGA – time groups analysis (Gibbon 2013)

▫ implemented as AnnotationPro+TGA (Klessa & Gibbon 2014)

▫ automatic segmentation into interpausal time groups

▫ extraction of linear regression intercept and slope

intercept: correlates with segmental duration

slope: tendency towards deceleration/acceleration, i.e. average increase/decrease in duration over the pause-defined segment


Methodology

• nPVI-V & rPVI-C (Grabe & Low 2002)

▫ automatic segmentation into C and V intervals based on phonetic alignment

▫ extraction of interval durations and calculation of PVIs for interpausal time groups using special plugin for AnnotationPro


Results• 1265 inter-pausal groups

• min. average of 38 groups, max. average of 64 per speaker

• more groups produced at slower than at faster rates


Intended vs. realized tempo• mean speaking rates (syl/sec) realized by the

speakers in agreement with the assumptions of the recording scenarios:▫ the fastest in the vfast condition and the slowest in the

vslow condition▫ after normalization by speakers and files - slightly

higher rates in the sent subcorpus than in poetic verses (poe) and read text (text)


Mean values of speech rates (syll/sec) realized by the speakers.

Results: statistics (multivariate ANOVA)

variable F df pspeaker 3.83 8 0.00

style 5.84 4 0.00

tempo 15.81 8 0.00

speaker*style 0.71 16 0.78

speaker*tempo 1.84 32 0.00

style*tempo 1.13 16 0.32

speaker*style*tempo 1.03 64 0.42

TGA results• The mean values of regression intercepts:

▫ positive correlations with segmental durations▫ negative correlations with speaking rates▫ confirmed for the present data, except fot vfast, text


Mean values of intercept across speaking styles & tempi.

• The mean values of regression slopes:▫ slightly higher at normal or slower rates than at the very

fast or fast rates, depending on the speaking style▫ more deceleration at slower rates more dynamics in

within-group timing▫ grater differences in slopes in poe and text than in the

sent subcorpus


TGA results

Mean values of slope across speaking styles & tempi.

Durational variability and tempo

ANOVA

style: F=15.9; p<0.01

tempo: F=66.34; p<0.01

style*tempo: F=1.69; p=0.04

nPVI-V

style: F=16.32; p<0.01

tempo: F=2.52, p=0.04


rPVI-C

style: F=16.77; p<0.01

tempo: F=146.6; p<0.01



Durational variability and style

ANOVA: F=6.19; p<0.01

nPVI-V: F=6.62

rPVI-C: F=7.08

cf. nPVI-syl between 35 and 40 (Gibbon et al. 2007);

nPVI-V=42.1 (Wagner 2014) and 46.6 (Grabe & Low 2002);

rPVI-C=68.1 and 79.1 (Wagner 2014, Grabe & Low 2002)


Rhythmic classification of Polish

SENT TEXTPOOLE

DEnglish 24 19 22.3Germa

n 21.4 14.1 16Italian 9.1 16.2 11.1

Spanish 8.2 12.2 9Euclidean distances between Polish and other languages (based on Arvaniti 2012).


Summary• First insights into a new “speech

timing”/”rhythmic” corpus:▫ variability in speech rate measures in agreement

with the intended five tempo conditions▫ higher intercepts at slower rates, weaker effect of

style and speaker ( speaker normalization)▫ interaction between speaking style & slope:

differences between poe, text and sent subcorpora, less variability in sent


Summary• PVI analysis:

▫ generally very low durational variability of V intervals (cf. data for Spanish or Italian, Arvaniti 2012)

▫ differences in the nPVI-V due to speaking style related to different timing pattern in the jambic poem

▫ nPVI-V stable at different tempi▫ higher durational variability of C intervals at slower rates▫ rPVI-C sensitive also to speech style differences (lower in

sent)▫ greatest similarity of Polish timing to Spanish (the lowest

euclidean distances)▫ from among English, German (stress-timed), Spanish and

Italian (sylable-timed) Polish displays the greatest durational variability among speaking styles as measured by PVIs


Future work• analyses based on the whole corpus• normalization of speaking rates among speakers• application of different measures of timing patterns

(e.g. other TGA measures, Wagner P. 2008)• duration is not everything!


AcknowledgementsProject “Rhythmic structure of utterances in Polish: A corpus study” from National Science Center (NCN) for years 2014-2016, project number: 2013/11/D/HS2/04486.

Special thanks to:Jolanta Bachan (Institute of Linguistics, AMU)annotators & speakers

References1. Wiget, L., White, L., Schuppler, B., Grenon, I., Rauch, O. &Arvaniti, A. (2012). The usefulness of metrics in the

quantification of speech rhythm. Journal of Phonetics, 40(3), 351-373.2. Barry, W. J., Andreeva, B., Russo, M., Dimitrova, S., and Kostadinova, T. (2003). Do rhythm measures tell us anything

about language type? D. Recasens, M. J. Solé and J. Romero (eds.) Proceedings of the 15th International Congress of Phonetic Sciences, Barcelona, Spain: 2693–2696.

3. Dauer, R. M. (1983). Stress-timing and syllable-timing reanalyzed. Journal of Phonetics, 11, 51-62.4. Dauer, R. M. (1987). Phonetic and phonological components of language rhythm. Proceedings of the XIIth ICPhS, Tallinn,

Estonia, (pp. 447-449).5. Dellwo, V. (2006) Rhythm and speech rate: A variation coefficient for deltaC. In P. Karnowski & I. Szigeti (Eds.), Language

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6. Dellwo, V. (2010). Influences of speech rate on the acoustic correlates of speech rhythm: An experimental phonetic study based on acoustic and perceptual evidence. Universität Bonn, Bonn University. Bonn, Germany.

7. Dellwo, V., Wagner, P. 2003. Relations between language rhythm and speech rate. Proc. 15th ICPhS Barcelona, 471-474.8. Klessa, K. and Gibbon, D. (2014) Annotation Pro + TGA: automation of speech timing analysis, P roc. of the 9th Language

Resources and Evaluation Conference , Reykjavik, Iceland. ISBN 978-2-9517408-8-4.9. Gibbon, D. (2013). TGA: a web tool for Time Group Analysis. Proc. of Tools and Resources for the Analysis of Speech

Prosody (TRASP). Aix-en-Provence, 30 August 2013.10. Yu, J., Gibbon, D., & Klessa, K. (2014). Computational annotation-mining of syllable durations in speech varieties. In

Proceedings of 7th Speech Prosody Conference (pp. 20-23).11. Grabe, E., & Low, E. L. (2002). Durational variability in speech and the rhythm class hypothesis. Laboratory Phonology 7,

515-546. Berlin: Mouton de Gruyter.12. Malisz, Z. (2013). Speech rhythm variability in Polish and English: A study of interaction between rhythmic levels.

Niepublikowana rozprawa doktorska. Wydział Anglistyki, Uniwersytet im. Adama Mickiewicza w Poznaniu.13. Ramus, F., Nespor, M., & Mehler, J. (1999). Correlates of linguistic rhythm in the speech signal. Cognition, 73, 265-292.14. Russo, M., & Barry, W. J. (2008). Isochrony reconsidered. Objectifying relations between rhythm measures and speech

tempo. In Proceedings of Speech Prosody (Vol. 4, No. 08, pp. 419-422).

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