using speech recognition as an assessment tool to...

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Proceedings of the 2016 Conference for Industry and Education Collaboration,

Copyright ©2016, American Society for Engineering Education

Using Speech Recognition as an Assessment Tool

To Enhance Language Learning

Don Ploger, Rosemary Rahill

Florida Atlantic University

Abstract

This case study describes how the Google speech recognition program helped a 63-year-old man

improve his pronunciation. The learner had taken eight years of French in school and understood

approximately 2000 words. The instructor is an American who spent eight years in France, and

then five years teaching college French in the US.

The instructional materials consisted of grammatically correct sentences using each of the 100

most common French verbs. Based upon these results, the instructor selected ten challenging

sentences. On the pretest, the learner’s accuracy rate was 60.7% as measured by Google and

77.5% as measured by a native speaker. The instructor then provided specific feedback to help

improve the pronunciation. On the posttest, the learner’s accuracy improved on both measures:

Google (80.9%); native speaker (96.6%).

The results indicate that Google Translate provides feedback that can potentially help a foreign

language learner. There are, however, limitations to the current form of the software. For many

words, the native speaker easily understood the learner, but the current version of Google

Translate did not. The combination of speech recognition with expert human assistance proved to

be effective in enhancing the learner’s performance.

Introduction

Engineering shaped a century and changed the world.

National Academy of Engineering 1

Engineers build powerful tools that make life better for people.2 Computer speech recognition is

one such tool. In its current state, speech recognition is remarkably accurate for native speakers,

but far less helpful for a language learner. In this case study, we describe instructional methods

that used an existing speech recognition program to help a 63-year old American improve his

French pronunciation.

Any tool, no matter how useful in the present moment, has a past and a future. Speech

recognition has a long history and many promising possibilities. It is possible to make speech

recognition useful for the language learner. This will require the work of experts, but it does not

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require a revolution.

The development of the modern computer required several revolutions – and an enormous

amount of work by many engineers. Although many people see the computer as a finished

product, it is actually the result of many inventions. Half a century ago, the computer was

difficult to use, even for experts. Engineers have made computers available to everyone, even

children.

In 1951, the UNIVAC became the world's first commercially available computer. In November

1952, CBS used UNIVAC to predict the winner of the presidential election. Early in the evening,

UNIVAC projected that Dwight Eisenhower would win, but CBS withheld this prediction. As

the night went on, Walter Cronkite announced, on national television, that UNIVAC was right.3

Despite its remarkable success, UNIVAC had serious drawbacks. It was 50 feet long and

contained more than 5,000 vacuum tubes. As a result, it was very expensive to purchase,

maintain, and operate. Only 100 units were sold in five years.4

While UNIVAC showed that a computer could be a commercial product, IBM proved that it

could be a commercial success. The IBM 360 was one of the most influential computers in

history, and IBM went on to dominate the computer market for decades.5 However, it was

extremely expensive: in 1968, the IBM 360 cost $253,000 (adjusted for inflation, $1.6 million).

In December 1968, Douglas Engelbart gave a presentation, which is now called “The Mother of

All Demos,” showing a computer with a graphical interface.6 He typed to the computer and the

display changed immediately, which was impossible with commercial computers of the day.7

Even more amazing, he could control the video screen with a pointing device, which he had

patented. The demo needed the combined power of several University computers.8 As a result,

most experts believed it was impossible to make a useful computer that people could afford.9

More than a decade later, Xerox introduced the Star, the first product with a mouse-driven

interface, with many features from Englebart’s demo. Because the Star was expensive, sales

were not good, and production ceased.10

In 1984, Apple launched the Macintosh, the first successful computer with a graphical user

interface. Microsoft clearly recognized the potential of the Macintosh, and they built their own

version.11 They used ideas from Englebart’s demo, and they learned from the products created

by Xerox and Apple. Windows now has more than 90% of market share.12

Although the mother of all demos provided the inspiration, it took more than 15 years to develop

a successful commercial product. There were important reasons for this long delay. In 1968,

computers were not powerful enough to operate the necessary programs. Furthermore,

computers were far too expensive for the general public.

Success of the Macintosh (and Windows) depended upon two revolutions:

1. Computers became much faster and more affordable.

2. A completely new market opened: tens of millions of people bought computers.

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In the three decades following those revolutions, the exponential growth of computer power

continued. In 1984, the Macintosh had 128 kilobytes of RAM, and cost $2495 (adjusted for

inflation $5,730).13 Now, a Mac has 8 GB of RAM (60,000 times more memory) and costs

$1,099. There has been a 6 million per cent increase in memory and an 80% decrease in price.

Although the cost of almost every other product has increased, the price of a computer has

decreased substantially. Because of inflation $253,000 in 1968 had the same (overall) buying

power as $1.6 million today.14 However, the computing power that cost $253,000 in 1968, could

be purchased for less than $100 today. Moore’s Law, a mathematical equation that predicts the

cost of computer memory over time, can explain this. Gordon Moore, a co-founder of Intel,

observed that the number transistors in an integrated circuit doubled every two years. Moore’s

prediction has proved to be useful for half a century: the result is greatly increased power at a

substantially lower cost than earlier computers.15

Speech recognition Speech recognition has been one of the biggest challenges in the history of engineering. The

problem proved to be much more difficult than the graphical interface. To illustrate this contrast:

In 1963, five years earlier than Englebart’s demo, Ivan Sutherland created Sketchpad, a graphical

computer program in which you could change a geometric shape on a computer screen using a

light-sensitive pen.16

Timothy Johnson gave a demo of Sketchpad for a television program. He said: “We’re going to

show you a man actually talking to a computer in a way far different than it’s ever been possible

to do before.”17

The journalist exclaimed: “Surely not with his voice!”

Johnson replied, “No, he’s going to be talking graphically.”

Even after the graphical interface had become a successful commercial product, speech

recognition remained in a primitive state. Engineers were able to reduce the vocabulary size to

achieve a certain degree of accuracy.18 Gradually, the accuracy has improved. Over the past

three decades, progress has been remarkable. The result can be stated in simple words: you talk;

it types.

Under certain conditions, there are still problems with accuracy, even for native speakers. In a

recent study, American students spoke English at a fast pace while playing a computer game.

The speech recognition program made a large number of errors. One student said that the errors

“made me less engaged, because I felt like it was counting off for something I knew”. There is

no it to give or deny credit: only a computer program that is either accurate or not.19

To understand the effect of false-negative errors on motivation, it is useful to consider attribution

theory. People tend to see a cause and effect relationship even where there is none.20 In a classic

study, Heider and Simmel showed college students a video of simple moving objects. Most

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students interpreted the circles and triangles as characters with human thoughts and feelings: the

big triangle was a “bully”; the small triangle and the circle were “in love.”21

People perceive the computer, not as a program that runs on a particular machine, but as another

person. When false negative errors occur, there is a strong tendency to think that the “person”

was unhelpful. This illusion is part of the human condition, and if engineering tools are to be

helpful, it is necessary to make use of this illusion.

Learning a Language

Research indicates that there is a critical period for learning a language.22 Learning a language is

easy during the first three years of life.23 After age 7, however, it becomes more challenging,

and for adults the process can be very difficult. The critical period does not imply that it is

impossible to learn a second language, but it does mean that powerful methods are needed.24

William Alexander described his attempt to learn French as an adult.25 He had studied French

for 6 years in school, and stopped after sophomore year of high school. He did not take any

foreign language in college. He travelled to Europe after graduating and found that he could not

speak French.

Decades later, at age 56, he decided to visit France again. In preparation for the visit, he spent

900 hours over 13 months (more than two hours a day), using many methods, including:

• All 5 levels of Rosetta Stone

• All 5 levels of Fluenz

• 2 complete sets of Pimsleur audio lessons

• 52 weeks of a PBS course in French

When he visited France with his wife, he found that he had forgotten a surprising amount of

French. In fact, his wife (who had no formal training in French) sometimes remembered a word

that he had forgotten. In describing the results: “Not only had I failed to become fluent, or even

conversant in French, I failed spectacularly more than I imagined possible.”25

Alexander’s work, and especially his honesty about his difficulties, are relevant to research on

language learning. It is useful to examine his approach in some detail. He did not master the

basic language of politeness. He did not verify that mastery with a native speaker. Finally, he

did not use speech recognition, which has the power, at each moment, to alert a language learner

to difficulties.

Speech recognition can help language learners, but current systems are limited. Often, the learner

says a word that is understood by a native listener, but not by the program. It is important to

reduce these false negative errors because they decrease motivation. In speech recognition,

reducing vocabulary size will increase accuracy. For native speakers, current programs are so

efficient that it is possible to consider all of the words in the dictionary and still have a

remarkably high accuracy rate. For language learners, however, there is a very different set of

tradeoffs. The learner does not need the full dictionary. For most bilingual adults, a 3000-word

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vocabulary provides a good basis for language use.26 This is less than 5 per cent of the full

dictionary.

In its current form, Google types a word, and it is either right or wrong. This is ideal for a native

speaker, but not for a language learner. Let us consider an analogy to marksmanship. The U.S.

Army defines four levels of accuracy: Expert, Sharpshooter, Marksman, and Unqualified.27 If a

simple pass-fail grade were given, soldiers would be classified as either Expert or Unqualified,

which would not provide sufficient feedback to support military training.

It is possible to construct a speech recognition scale similar to that of marksmanship. It is

important to designate when the learner has hit the bullseye and to note when he has missed the

broad side of the barn. It is also useful to define levels between these two extremes.

Goals for Learning a Language

When visiting Paris, the most popular tourist destination in the world, one possible goal is to

speak only English and not even bother to learn Bonjour. This is hardly inspiring. After all,

Bonjour is in the English dictionary.

A more ambitious goal is to speak any word in the dictionary with native level proficiency. For

people who can achieve this, it is very impressive. However, in light of the critical period for

learning a language, this goal is simply not realistic for most adults.

This research aims to find a middle way. It begins with the basic language of politeness and

emphasizes excellence, though not perfection, in pronunciation. When difficulties are

encountered: attempt to remedy the pronunciation. If difficulties persist: simplify the sentences

in order to achieve success as measured by an existing speech recognition program.

This study will collect data on the learner’s pronunciation, which will be assessed by both

Google Translate and a native speaker. Based upon this data, we will develop an accuracy scale

for pronunciation to help the language learner.

Methods

The learning materials consisted of 100 grammatically correct sentences using each of the most

common French verbs. The total word count was 754.

The learner read aloud the complete list of 100 sentences, and this was assessed by Google.

Based upon the results, the instructor selected 10 challenging sentences (Appendix 1). The

learner recited the 10 sentences in a pretest, with the performance assessed by both Google and a

native speaker. The instructor developed techniques to improve the learner’s pronunciation.

After practice the learner repeated the process on a posttest using the 10 challenging sentences.

Furthermore, the learner constructed 10 simplified versions (Appendix 2) of the challenging

sentences, which the instructor verified for grammar. These simplified sentences were assessed

by both Google and the native speaker.

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Results

When the learner read aloud the complete list of 100 sentences, Google Translate had an

accuracy rate of 76.5%. The instructor reviewed the data and selected 10 challenging sentences

(total word count = 89).

Table 1 shows that on the pretest, Google had an accuracy rate of 60.7%. This accuracy rate

was substantially lower that the overall rate for all 100 sentences. These sentences were then

tested with a native speaker, and the accuracy rate was 77.5%.

Pretest

Trial Words Errors Accuracy

Computer 89 35 60.7

Human 89 20 77.5

Table 1. Ten Challenging Sentences: Pretest

After the pretest, the instructor identified challenges, gave specific feedback, and then provided a

recording of accurate pronunciation. The learner practiced then took a posttest.

At posttest, using the identical 10 challenging sentences, the accuracy improved to 80.9% by

Google Translate and 96.6% by a native speaker (Table 2). The increase in accuracy with

Google was 16.8%, while the increase in accuracy with the native speaker was 15.7%.

Table 2. Ten Challenging Sentences: Posttest

Simplified Sentences

The learner simplified the sentences by selecting the words that Google consistently recognized.

The instructor verified the resulting 10 simplified sentences for grammatical correctness. Once

constructed, these sentences were tested without any additional practice.

Table 3 shows the results with both methods of evaluation: 98.7% accuracy with Google and

100.0% accuracy with a native speaker.

Posttest


Computer 89 17 80.9

Human 89 3 96.6

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Table 3. Ten Simplified Sentences

On the pretest for the selected sentences, there were 20 words that the native speaker did not

recognize. (Google did not recognize any of these 20 words.) On the other hand, for these same

sentences, there were 15 words that the native speaker understood, but Google did not.

This pattern continued on the posttest for the selected sentences. The native speaker understood

all but three of the words. Google did recognize these three, as well as an additional 14 that the

native speaker understood.

Throughout the entire study, including all 754 words, there was only one occasion where Google

recognized a word that the native speaker did not. This was corrected in a single repetition.

Color Code Classification

The data were analyzed using the following color code classification scheme:

Blue: Google recognized the word (the Native Speaker recognized each of these words).

Green: Native Speaker recognized the word (but Google did not).

Red: Neither recognized the word.

Table 4 shows the color-coded results for the Pretest.

Table 4. Color-Coded Results for the Ten Challenging Sentences: Pretest

Table 4 corresponds to Table 1: 60.7 percent of words were correctly recognized by Google, and

Ten Simplified Sentences


Computer 66 1 98.5

Human 66 0 100.0

Pre-test

Color Per Cent

Blue 60.7

Green 16.8

Red 22.5

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therefore coded Blue. A total of 77.5 percent of words were recognized by the native speaker.

By subtraction, this gives 16.8 percent of the words that were recognized by the native speaker

but not by Google, and therefore coded Green. A total of 22.5 percent of the words were not

recognized by either human or computer, and therefore coded Red.

Table 5 shows the color-coded results for the Posttest. This corresponds to Table 2: 80.9

percent of the words were correctly recognized by Google, and therefore coded Blue. A total of

96.6 percent of the words were recognized by the native speaker. By subtraction, this gives 15.7

percent of the words that were recognized by the native speaker but not by Google, and therefore

coded Green. A total of 3.4 percent of the words were not recognized by either human or

computer, and therefore coded Red.

Post-test

Color Per Cent

Blue 80.9

Green 15.7

Red 3.4

Table 5. Color-Coded Results for the Ten Challenging Sentences: Posttest

Table 6 shows the color codes for the simplified sentences.

Ten Simplified Sentences

Color Per Cent

Blue 98.5

Green 1.5

Red 0

Table 6. Color-Coded Results for the Ten Simplified Sentences

For the simplified sentences, the native speaker understood each word spoken. Google

understood all except one.

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Discussion

Learning a new language is difficult, and many adults fail despite much hard work. Engineering

can help in many ways. Google Translate, an existing speech recognition program, provided

rigorous feedback for a learner’s pronunciation. In the entire study, there was only one example

when Google recognized a word but the native speaker did not, and that was corrected by a

single repetition. If Google understands you, a native speaker almost certainly will.

The current version of Google Translate is highly accurate for native speakers. It also provides

useful feedback for language learners, even though it is not intended for that purpose. In light of

the substantial technical difficulties, it is not surprising that there were examples when the native

speaker understood the learner, but Google did not. The sounds that Google did not recognize

are typical of pronunciation challenges encountered by Americans learning French. The

instructor provided effective feedback that led to improvement. Although this learner was

frustrated during the early phase of the study, the instructor helped simplify the materials. With

practice, the learner became more accurate and more confident.

In this study, each word was color-coded to designate accuracy. Words recognized by Google

Translate were colored blue; those recognized by the native speaker (but not by Google) were

colored green; and those not recognized by either were colored red. This color-coded

assessment provided the learner with useful feedback, but not in a timely manner.

Based upon these results, we will revise our approach to limit the number of difficult sounds, so

that the learner can achieve good results more effectively using the existing version of Google

Translate. The results clearly show that simplifying the sentences leads to improved accuracy.

This method can be applied to a wider range of useful sentences, but it cannot be extended

indefinitely. It is necessary to include some words, which learners can pronounce well enough,

but yet still fall short of the high standard set by Google Translate.

In facing any new problem, it is useful to consider the successes of the past. Doug Englebart and

his colleagues tried to develop a graphical interface using the computers of 1968. That problem

was solved, but it took two decades, and required a revolution in electronic engineering. The

result changed the world, and made possible speech recognition that is accurate for native

speakers.

Although current speech recognition programs give useful feedback, language learners need

tools that are much more flexible. Constructing these programs would certainly require the work

of expert engineers, but in this case, no revolution is needed. Computers are already powerful

enough. Engineers already have all the necessary skills. Many potential customers could

benefit from this.

The key to the solution is well known: Decreasing vocabulary size improves the accuracy of

speech recognition programs. Because a language learner’s vocabulary is less than five per cent

of the words in the dictionary, there would be great benefits if speech recognition programs

could be adjusted to suit these needs.

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This study demonstrates that Google Translate can be useful for language learners. It is

necessary, however, to improve accuracy. It is possible to decrease the false negative errors,

nearly to zero, but this could increase false positive errors – reporting that the pronunciation was

good, when in fact improvement was needed. Google Translate already provides a rigorous

assessment of the learner’s pronunciation. If a speech recognition program also conducted a

second assessment, using a smaller vocabulary, a language learner would get useful feedback,

even when improvement was needed. These adjustments would lead to powerful tools for

language teachers.

We all react to the computer as if it were another person. If we are understood, despite our

failings, there is strong motivation to continue practice. Combining state-of-the-art engineering

with excellent teaching methods would help learners achieve the challenging goal of speaking a

foreign language.

Acknowledgements: The authors thank Maikane Deroo for many helpful suggestions

throughout the course of this study. We thank Myriam Ruthenberg, Associate Professor of

Italian, Director Undergraduate Studies, for encouraging our collaboration. We thank Roger

Hawkins for insightful feedback. We thank Robin Mcdaniel for careful editing of the

manuscript.

Bibliography

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DON PLOGER, Ph.D., is an Associate Professor of Education at Florida Atlantic University. He is interested in how

people solve novel problems and how essential mathematical knowledge is communicated to non-engineers. He is

also involved in researching how people learn foreign languages using computer speech recognition and translation

tools. [email protected]

ROSEMARY RAHILL, M.A.T., is a visiting instructor of French at Florida Atlantic University. As an avid

proponent of the myriad advantages of studying and learning a foreign language, in particular French, she is

dedicated to conveying this message to students and encouraging them to become life long learners of French. She is

also interested in finding new tools and methods of instruction to increase levels of motivation.

Appendix 1: Challenging sentences

Total word count: 89

Il faut beaucoup réviser pour apprendre une langue étrangère.

Much review is required to learn a foreign language.

Savez-vous où se trouve cette rue?

Do you know where this street is located ?

J’attends le bus depuis une heure!

I have been waiting for the bus for an hour !

Il faut passer par la place de l’Etoile pour visiter l’Arc de Triomphe.

You need to cross the Place de l’Etoile to visit the Arch of Triomph.

Je trouve que Paris est une si belle ville!

I find Paris to be such a beautiful city !

Nous aimons découvrir l’histoire des monuments que nous visitons.

We love to discover the history of the monuments that we visit.

Je mets un pull parce qu’il fait un peu frais.

I am wearing a sweater since it is a bit cool.

http://www.simplypsychology.org/attribution-theory.html

http://ilabs.uw.edu/institute-faculty/bio/i-labs-patricia-k-kuhl-phd

http://www.robwaring.org/papers/CUP/cup.html

http://www.armystudyguide.com/content/army_board_study_guide_topics/m16a2/trainers-guide-to-success.shtml

mailto:[email protected]

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Où est-ce que je rends les clés, s’il vous plait?

Where do I turn in my keys, please ?

Tu connais cette rue?

Do you know this street ?

Ils vont arriver après nous.

They will arrive after us.

Appendix 2: Simplified sentences

Total word count: 66

Je voudrais apprendre une langue étrangère.

I would like to learn a foreign language.

Savez-vous où se trouve la salle de bain ?

Do you know where the bathroom is located?

J’attends le bus.

I am waiting for the bus.

Nous voulons visiter l’Arc de Triomphe.

We want to visit the Arch of Triomph.

Je trouve que Paris est une belle ville!

I find Paris to be a beautiful city !

Nous aimons découvrir l’histoire des monuments que nous visitons.

We love to discover the history of the monuments that we visit.

Il fait un peu frais.

It is a bit cool outside.

Où est le journal, s’il vous plaît?

Where is the newspaper, please ?

Tu connais cette musique?

Do you know this music ?

Voulez-vous aller avec nous?

Do you want to go with us ?

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