Pronunciation Scoring For Language Learners Using A Phone Recognition

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Presented by,

K.L Srinivas (M.Tech 2^nd year)

CS626-460: Lecture 34

Pronunciation Scoring For Language Learners Using A Phone Recognition

System

K.L Srinivas (M.Tech 2^nd year) Guided by,

Prof. Preeti Rao (Elect. Dept)

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Introduction

Pronunciation refers to the manner in which a particular word of a language is uttered.

Motivation

Accurate pronunciation or articulation is a vital component of a language acquisition process.

Fluency in speech of a non-native speaker of a language can be judged by pronunciation and prosody.

Department of Electrical Engineering , IIT Bombay 2

by pronunciation and prosody.

Non availability of a classroom environment for learners.

Subjective Evaluation

Word spoken: Kaleidoscopic

Speaker 1 Speaker 2

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Problem statement

Developing computer based automatic pronunciation scoring system.

Accessing the closeness of language learner pronunciation to that of reference speaker (already stored in system).

To provide language learner with pronunciation score and feedback.

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A brief on Automatic Speech Recognition

Recognition

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Introduction

Automatic speech recognition (ASR) is a process by which an acoustic speech signal is converted into a set of words.

Getting a computer to understand spoken language.

Approaches to ASR

Template matching

Knowledge-based (rule based approach) Knowledge-based (rule based approach) Statistical approach (machine learning)

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Statistical based approach :

Collect a large corpus of transcribed speech recordings.

Train the computer to learn the corresponding instances (Machine learning).

At run time, apply statistical processes to search through the space of all possible solutions and pick the statistically most likely one.

one.

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Speech recognition tool kits :

Sphinx and HTK are two widely accepted and used speech recognition tools.

– CMU sphinx : Carnegie Mellon University (CMU) – HTK : Cambridge University

Both the frameworks are used for developing, training and testing a speech model from existing corpus speech data.

testing a speech model from existing corpus speech data.

Both use Hidden Markov Modeling techniques.

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MFCC feature vector :

• The Mel-Frequency Cepstrum Coefficients (MFCC) is a popular choice

• Frame size : 25 msec

• Hop size : 10 msec

• 39 feature per 10ms frame

• Absolute : Log Frame Energy (1) and MFCCs (12)

• Delta : First-order derivatives of the 13 absolute

• Delta : First-order derivatives of the 13 absolute coefficients

• Delta-Delta : Second-order derivatives of the 13 absolute coefficients

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Sphinx 3 :

Training :

Testing / Decoding:

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Decoder ouput :

Recognition Hypothesis :

– This gives the single best recognition result for each utterance processed.

– Linear word sequence with their time segmentation and their scores.

Output format :

<word> <start frame> <end frame> <AScr> <LM Score>

<AScore +LM Score> <Ascale>

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Non-native speech characters:

Phone substitutions: S in word ‘she’ pronounced as s Phonotactic constraints: Stop cluster sk in school

pronounced as iskUl

Use of language model masks out the non-nativeness during recognition.

Automatic Speech Recognition for non-native speech

recognition.

Accuracy of state-of-the-art phone recognition systems as low as 50%-70%

Traditional ASR techniques cannot be used for non-native speech

Phone recognition to be carried out in constrained mode

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Back to pronunciation scoring

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Pronunciation Scoring System

Generation of Pronunciation

Variants

Constrained Phone Decoder

Variant Boundary

Refinement and Canonical

transcription of the utterance

Input speech signal

Variant Selection Refinement and

Prosodic analysis

Prosody Score Articulation Score

Pronunciation Score

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Pronunciation Variants

Challenges

Canonical form: SIL f aa n d aa m ee n’ clt t aa l s SIL

Variant_1: SIL f aa n d a m ee n’ clt t aa l s SIL Variant_2: SIL f aa n d ee m ee n’ clt t aa l s SIL

Word: Fundamentals

Challenges

• No ready database of speakers of Indian English

• Multiple L1s for Indian speakers poses further challenges.

• Native Hindi and native English databases are available

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Constrained Phone Decoding

HMM based recognizers

HTK 3.4 Sphinx 3

Decoding

Extraction of Decoder in

Acoustic Models Extraction of

MFCC Feature Vectors

Decoder in Forced Alignment

Mode Input Speech

Utterance

Acoustic Models from training Variants

Aligned Phone Sequence with likelihood for each

variant

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Variant Selection

Select Variant with the highest likelihood Aligned Phone Sequence

with likelihood for each variant

Visual Feedback and Articulation Score

Strik and Cucchiarini (2000): Pronunciation variations and modeling

Goronzy, Rapp and Komp (2004 ): Non-native pronunciation variations and

generation ( native English speakers speaking German)

Wesenick and Schiel (1994 ), Wesenick (1996): Generation of rules for German pronunciation variations

Franco et al. (1997) , Franco et al. (2000): A paradigm for automatic assessment of pronunciation quality.

Witt and Young (2000): Presented likelihood based goodness of pronunciation scheme

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Databases

TIMIT database

• 630 speakers of 8 major dialects of American English.

• Each speaking 10 phonetically rich sentences.

TIFR database

• 100 native speakers of Hindi.

• Each speaking 10 phonetically rich sentences.

Indian English database - Testing

• 30 Indian college students each speaking the 2 common sentences

Training

• 30 Indian college students each speaking the 2 common sentences from TIMIT database.

47 class TIMIT models: Entire phone set from TIMIT.

52 class Union models: Entire TIMIT phone set(47 phones) and 5 additional phones from the TIFR phone set making a total of 52

phones.

48 class Union models: Entire TIFR Hindi phone set(36 phones) and 12 phones from TIMIT.

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Experiments and Evaluation

The focus of this work is to investigate the effect of selection of phone models from one of 47, 52-union and 48- union phone

models

Method I: The number of instances in which the surface

transcription is within the top N decoded sequences in terms of

transcription is within the top N decoded sequences in terms of likelihood score.

Method II: The edit distance between the most likely phone sequence and the surface transcription in terms of %correct and

%accuracy

Method III: Normalized likelihood error. A value of “0” for this measure indicates the best achievable performance.

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Performances of Method I and II

Tabulation of Method I and Method II of evaluation for HTK 3.4 and Sphinx 3

HTK 3.4 Sphinx3

Method I Method II Method I Method II

Decoder models

# of Unique variants

Reference transcription in

%Corr %Acc Reference transcription in

%Corr %Acc

Top 1 Top 5 Top 1 Top 5

SA1 SA1

47class 636 5 7 82.4 79.4 2 6 83.8 80.2

52class 1263 6 9 81.8 80.0 1 6 82.5 78.4

48class 763 21 24 96.2 94.6 12 17 92.2 89.1

SA2 SA2

47class 1026 6 11 88.0 85.8 5 9 88.8 86.4

52class 1026 7 13 88.6 87.0 5 10 85.9 83.8

48class 1026 16 20 92.8 92.2 7 9 89.4 87.7

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Performance of Method III

Distribution of the likelihood scores across the 60 utterances

48-phone class has average likelihood error closest to zero of the three phone sets.

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Articulation Scoring methods :

Articulation score indicates the closeness of language learner’s pronunciation with native speaker (of target language) pronunciation.

Detects phoneme level mispronunciation and extent to which phoneme has been mispronounced.

Algorithm uses speech models derived from speech database of native speakers.

speakers.

Uses forced align tools in the background to get acoustic scores (quantitative measure indicating acoustic fit for that particular speech segment).

Two methods investigated:

• GOP (Goodness of Pronunciation) score [2].

• Method by Sunil K. Gupta [9].

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GOP scoring method :

Confidence with which particular phone has been recognized . Also called as Goodness Of Pronunciation (GOP) score.

GOP score is given by normalized log posterior probability

( )

(

^{( )}

)

( ) log | ^p ( )

GOP p = P p O NF p

( )

( ) ( )

| ( )

( ) log ( )

max | ( )

p p q Q

P O p P p

GOP p NF p

P O q P q

∈

 

 

=  

 

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GOP scoring method (cont.) :

( )

(

^{( )}

) ( ⁽

^{( )}

⁾ )

log | log max |

( ) ( ) ( )

p p

q Q

p g

P O p P O q

GOP p l l

NF p NF p

= − ∈ = −

Block diagram for Articulation scoring

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Method by Sunil K. Gupta :

Shortcoming of GOP score :

• Threshold selection was based on subjective rating of human judges.

• Not providing any quantitative measure to measure extent of mispronunciation.

• Free decoder not accurate enough leading to alignment errors.

In this method two speech models have to be prepared:

• 48 class phone models ( 36 TIFR Hindi + 12 TIMIT English)

• Garbage model ( all phonemes of speech data combined to get one speech model)

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Garbage model :

A single speech model combining all the phonemes of speech data.

Entire speech corpus trained with garbage transcription.

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Methodology (cont.) :

Utterance is force aligned using Sphinx3_align with the reference transcription using 48 class phone models.

• Each phoneme of the transcription will have its own acoustic score.

• These log-likelihood scores are duration normalized given by

q i

l for q

Similarly, utterance is force aligned with garbage transcription using Garbage model.

Difference between these two likelihood is current phoneme likelihood

This difference score (d) is used for coming up with phone articulation score using lookup score table.(explained in next slide)

q g i

d = −l l for q

g i

l for q

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Formation of score table :

For each utterance “In-grammar” and “Out-grammar” is formed

• In-grammar : When the transcription is conforming to target acoustic waveform.

• Out-grammar : Transcription selected is some random phrase from training database not conforming to target acoustic waveform.

In-grammar and Out-grammar transcriptions are force aligned to come

In-grammar and Out-grammar transcriptions are force aligned to come up with log-likelihood scores:

• In-grammar :

• Out-grammar :

i i .

q g i

l and l for q

i i i

q g

d = −l l

o o .

q g i

l and l for q

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Score table (cont.) :

Using all the in-grammar points and out-grammar points , pdf is formed for each phoneme.

( , )

i i i

f = N µ σ

( , )

o o o

f = N µ σ

Using these probability density functions are used for coming up with score table .(table shown in results section)

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Results :

Histograms and Gaussian pdf (approximating data points) for both In- grammar and Out-grammar for phoneme “aa”:

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Results (cont.) :

Histograms and Gaussian pdf (approximating data points) for both In- grammar and Out-grammar for phoneme “ee”:

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Results (cont.) :

Combined PDF of In-grammar and Out-grammar for “aa” :

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Results (cont.) :

Combined PDF of In-grammar and Out-grammar for “ee” :

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Results (score table) :

Below calculations and table is for phoneme “aa” : f denotes probability density function.

and are In-grammar and Out-grammar mean respectively.

For In-grammar and Out-grammar points : ( ) log ⁱ( ) log ^o( ) h x = f x − f x µi µ^o

For In-grammar and Out-grammar points :

Score table for phoneme “aa” in next slide : ( ) log ( ) log ( ) 1.242

( ) log ( ) log ( ) 0.272

i i i o i

o i o o o

h f f

µ µ µ

= − =

= − = −

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Score table (phoneme “aa” ) :

D h (x) Score

1.242 100

1.185 90

0.748 80

( ⁱ) h µ log(90 / 10)

( ) log(10)

h µi

log(80 / 20)

( ) log(10)

h µi

0.457 70

0.219 60

0 50

log(10) log(70 / 30)

( ) log(10)

h µi

log(60 / 40)

( ) log(10)

h µi

log(50 / 50)

( ) 0

log(10)

h µi =

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Score table (phoneme “aa” ) :

D h (x) Score

0 50

-0.048 40

-0.1001 30

log(90 / 10)

( ) log(10)

h µo

log(80 / 20)

( ^o) h µ log(50 / 50)

( ) 0

log(10)

h µo =

-0.164 20

-0.259 10

-0.272 0

( ^o) h µ

( ) log(10) h µ log(70 / 30)

( ) log(10)

h µo

log(60 / 40)

( ) log(10)

h µo

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Result (Speaker 1 : Fundamentals) :

Phone Correct Pronun. Incorrect Pronun.

d Score d Score

h -8647 -90232

aa -14397 60% -2990 80%

n -99.5 -8224

d -23238 -27363

aa -15280 60% -40304 0%

m -756 -813

ee -19837 -19767

n’ -7571 -5941

SI -12570 -5250

t -2023 -6451

aa -8659.5 80% -10531 70%

l -10920 -2014

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Result (Speaker 2 : Fundamentals) :

Phone Correct Pronun. Incorrect Pronun.

d Score d Score

h 370 -9969

aa -10269 70% -10499 70%

n -9675 -4115

d -25162 -17556

aa -2295 80% -46778 0%

m -4100 -11144

ee -24043 -34685

n’ 4284 -5253

SI -4595 -10971

t -10534 -5146

aa -14271 50% -7271 80%

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Duration scoring :

Duration score provides feedback on normalized relative duration

difference between language learner speech and reference speaker speech.

Denotes whether a particular syllable is stressed or not.

If L_i and R_i are their respective durations corresponding to phoneme q_i then ,utterance consisting of N phones can be denoted by:

( , ,... ) for language learner speech

L = L L L

Normalized durations given by:

1 2

( , ,... _N) for language learner speech

L = L L L

1 2

( , ,... _N) for reference speaker speech

R = R R R

^ ^

1 1

and

i i

i N i N

i i

L R

= =

∑ ∑

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Duration scoring (cont.) :

Overall duration score given by :

Maximum duration score is ‘1’ and minimum is ‘0’.

^ ^

1

max 0, 1

N

i i

i

D L R

=

 

=  − − 



∑



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Duration scoring (Results) :

Speaker_1 was taken as reference and duration scores were calculated for other speakers.

Speaker_1

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Duration scoring (Results) :

Speaker_1 Vs Speaker_2

Duration score = 0.573 (low due to differences in ‘f’, ’a’ and ‘s’

duration)

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Duration scoring (Results) :

Speaker_1 Vs Speaker_3

Duration score = 0.485 (low due to differences in ‘f’, ’a’ and ‘s’

duration)

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Feedback , Articulation and Duration Score

Speaker 2

Canonical Transcription (Reference speaker) SIL f aa n d aa m ee n’ clt t aa l s SIL Speaker 1

Transcription

SIL f aa n d ee m ee n’ clt t aa l s SIL SIL f aa n d ee m ee n’ clt t aa l s SIL

Feedback

Articulation Score: 72% Duration Score: 0.573

SIL f aa n d ee m ee n’ clt t aa l s SIL

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References

1) Strik, H., Neri, A., and Cucchiarini, C. 2008. Speech technology for language tutoring. In Proceedings of LangTech ( Rome, Italy,February 28-29, 2008).

2) Witt, S., and Young, S. 2000. Phone-level pronunciation scoring and assessment for interactive language learning. Speech Communication. Vol. 30, pp. 95-108, 2000.

3) Franco, H., et al. 2000. Automatic scoring of pronunciation quality. Speech Communication.

Vol. 30, pp. 83-93, 2000.

4) Kawai, G., Hirose, K. 1998. A method for measuring the intelligibility and non nativeness of phone quality in foreign language pronunciation training. In Proceedings of ICSLP-98

(Sydney, Australia, November 30- December 04,1998) .pp. 1823-1826.

5) Goronzy, S., Rapp, S., Kompe, R. 2004. Generating non-native pronunciation variants for lexicon adaption. Speech Communication. Vol. 42, pp. 109-123, 2004.

6) Lee, K.F. 1998. Large-vocabulary speaker-independent continuous speech recognition: The SPHINX system. Ph.D. dissertation, Comput. Sci. Dep., Carnegie Mellon University.

7) Young, S., et al. 2006. The HTK Book v3. Cambridge University, 2006.

8) Samudravijaya, K., Rawat, K.D., and Rao, P.V.S. 1998. Design of Phonetically Rich

Sentences for Hindi Speech Database. J. Ac. Soc. Ind. Vol. XXVI, December 1998, pp. 466- 471.

9) Sunil K. Gupta, Ziyi Lu and Fengguang Zhao, “ Automatic Pronunciation Scoring for Language Learning ”, U.S. Patent 7,219,059, May 15, 2007.

Pronunciation Scoring For Language Learners Using A Phone Recognition

CS626-460: Lecture 34

Pronunciation Scoring For Language Learners Using A Phone Recognition

System

Introduction

Problem statement

A brief on Automatic Speech Recognition

Recognition

Introduction

Statistical based approach :

Speech recognition tool kits :

MFCC feature vector :

Sphinx 3 :

Decoder ouput :

Automatic Speech Recognition for non-native speech

Back to pronunciation scoring

Pronunciation Scoring System

Pronunciation Variants

Constrained Phone Decoding

Variant Selection

Databases

Experiments and Evaluation

Performances of Method I and II

Performance of Method III

Articulation Scoring methods :

GOP scoring method :

( )

(

)

( )

( )

GOP scoring method (cont.) :

( )

(

) ( (

) )

Method by Sunil K. Gupta :

Garbage model :

Methodology (cont.) :

Formation of score table :

Score table (cont.) :

Results :

Results (cont.) :

Results (cont.) :

Results (cont.) :

Results (score table) :

Score table (phoneme “aa” ) :

Score table (phoneme “aa” ) :

Result (Speaker 1 : Fundamentals) :

Result (Speaker 2 : Fundamentals) :

Duration scoring :

∑ ∑

Duration scoring (cont.) :

∑

Duration scoring (Results) :

Duration scoring (Results) :

Duration scoring (Results) :

Feedback , Articulation and Duration Score

References

) ( ⁽

⁾ )