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Presented by,K.L Srinivas (M.Tech 2nd year)Guided by,Prof. Preeti Rao (Elect. Dept)
Department of Electrical Engineering, IIT BombayMumbai , IndiaCS626-460: Lecture 34Pronunciation Scoring For Language Learners Using A Phone RecognitionSystem
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Introduction
Pronunciation refers to the manner in which a particular word of a language is uttered. MotivationAccurate 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. Non availability of a classroom environment for learners.
Subjective EvaluationWord spoken: KaleidoscopicSpeaker 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.
Department of Electrical Engineering , IIT Bombay#A brief on Automatic Speech Recognition
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 ASRTemplate matchingKnowledge-based (rule based approach)Statistical approach (machine learning)Department of Electrical Engineering , IIT Bombay#
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.
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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.
Both use Hidden Markov Modeling techniques.
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MFCC feature vector :
The Mel-Frequency Cepstrum Coefficients (MFCC) is a popular choiceFrame size : 25 msecHop size : 10 msec39 feature per 10ms frameAbsolute : Log Frame Energy (1) and MFCCs (12) 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: Department of Electrical Engineering , IIT Bombay#
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 :
Department of Electrical Engineering , IIT Bombay#Non-native speech characters:Phone substitutions: S in word she pronounced as sPhonotactic constraints: Stop cluster sk in school pronounced as iskUl Use of language model masks out the non-nativeness during recognition.Accuracy of state-of-the-art phone recognition systems as low as 50%-70%Traditional ASR techniques cannot be used for non-native speechPhone recognition to be carried out in constrained mode
Automatic Speech Recognition for non-native speechDepartment of Electrical Engineering , IIT Bombay#11Back to pronunciation scoringPronunciation Scoring SystemCanonical transcription of the utteranceInput speech signalProsody ScoreArticulation ScorePronunciation ScoreDepartment of Electrical Engineering , IIT Bombay#13Pronunciation VariantsChallengesNo ready database of speakers of Indian EnglishMultiple L1s for Indian speakers poses further challenges.Native Hindi and native English databases are availableCanonical form: SIL f aa n d aa m ee n clt t aa l s SILVariant_1: SIL f aa n d a m ee n clt t aa l s SILVariant_2: SIL f aa n d ee m ee n clt t aa l s SILWord: FundamentalsDepartment of Electrical Engineering , IIT Bombay#14Constrained Phone DecodingHMM based recognizers HTK 3.4 Sphinx 3
Decoding
Input Speech UtteranceAcoustic Models from trainingVariantsAligned Phone Sequence with likelihood for each variantDepartment of Electrical Engineering , IIT Bombay#15Variant SelectionPast WorkAligned Phone Sequence with likelihood for each variantVisual Feedback and Articulation ScoreStrik and Cucchiarini (2000): Pronunciation variations and modelingGoronzy, 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 schemeDepartment of Electrical Engineering , IIT Bombay#16Databases
TIMIT database630 speakers of 8 major dialects of American English.Each speaking 10 phonetically rich sentences.TIFR database100 native speakers of Hindi.Each speaking 10 phonetically rich sentences.Indian English database - Testing30 Indian college students each speaking the 2 common sentences from TIMIT database.Acoustic Phone Models47 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. TrainingDepartment of Electrical Engineering , IIT Bombay#17Experiments and EvaluationThe focus of this work is to investigate the effect of selection of phone models from one of 47, 52-union and 48- union phone modelsEvaluation Measures Method I: The number of instances in which the surface 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 %accuracyMethod III: Normalized likelihood error. A value of 0 for this measure indicates the best achievable performance.Department of Electrical Engineering , IIT Bombay#18Performances of Method I and IITabulation of Method I and Method II of evaluation for HTK 3.4 and Sphinx 3HTK 3.4Sphinx3Method IMethod IIMethod IMethod IIDecoder models# of Unique variantsReference transcription in%Corr%AccReference transcription in%Corr%AccTop 1Top 5Top 1Top 5SA1SA147class6365782.479.42683.880.252class12636981.880.01682.578.448class763212496.294.6121792.289.1SA2SA247class102661188.085.85988.886.452class102671388.687.051085.983.848class1026162092.892.27989.487.7Department of Electrical Engineering , IIT Bombay#We can see that 48 class has performed best for both the sentences as well as for both HTK and sphinx19Performance of Method III
Distribution of the likelihood scores across the 60 utterances48-phone class has average likelihood error closest to zero of the three phone sets.Department of Electrical Engineering , IIT Bombay#The results are consistent with method 1 and 220Articulation Scoring methods :Articulation score indicates the closeness of language learners 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.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].
Department of Electrical Engineering , IIT Bombay#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
Department of Electrical Engineering , IIT Bombay#GOP scoring method (cont.) :
Block diagram for Articulation scoringDepartment of Electrical Engineering , IIT Bombay#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)Department of Electrical Engineering , IIT Bombay#Garbage model : A single speech model combining all the phonemes of speech data. Entire speech corpus trained with garbage transcription. Department of Electrical Engineering , IIT Bombay#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
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)
Department of Electrical Engineering , IIT Bombay#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 up with log-likelihood scores:
In-grammar :
Out-grammar :
Department of Electrical Engineering , IIT Bombay#Score table (cont.) :Using all the in-grammar points and out-grammar points , pdf is formed for each phoneme.
Using these probability density functions are used for coming up with score table .(table shown in results section)
Department of Electrical Engineering , IIT Bombay#Results : Histograms and Gaussian pdf (approximating data points) for both In-grammar and Out-grammar for phoneme aa:
Department of Electrical Engineering , IIT Bombay#Results (cont.) : Histograms and Gaussian pdf (approximating data points) for both In-grammar and Out-grammar for phoneme ee:
Department of Electrical Engineering , IIT Bombay#Results (cont.) : Combined PDF of In-grammar and Out-grammar for aa :
Department of Electrical Engineering , IIT Bombay#Results (cont.) : Combined PDF of In-grammar and Out-grammar for ee :
Department of Electrical Engineering , IIT Bombay#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 :
Score table for phoneme aa in next slide :
Department of Electrical Engineering , IIT Bombay#Score table (phoneme aa ) :Dh (x)Score1.2421001.185900.748800.457700.21960050
Department of Electrical Engineering , IIT Bombay#Score table (phoneme aa ) :Dh (x)Score050-0.04840-0.100130-0.16420-0.25910-0.2720
Department of Electrical Engineering , IIT Bombay#Result (Speaker 1 : Fundamentals) :PhoneCorrect Pronun.Incorrect Pronun.dScoredScoreh-8647-90232aa-1439760%-299080%n-99.5-8224d-23238-27363aa-1528060%-403040%m-756-813ee-19837-19767n-7571-5941SI-12570-5250t-2023-6451aa-8659.580%-1053170%l-10920-2014
Department of Electrical Engineering , IIT Bombay#Result (Speaker 2 : Fundamentals) :PhoneCorrect Pronun.Incorrect Pronun.dScoredScoreh370-9969aa-1026970%-1049970%n-9675-4115d-25162-17556aa-229580%-467780%m-4100-11144ee-24043-34685n4284-5253SI-4595-10971t-10534-5146aa-1427150%-727180%l-10917-18839
Department of Electrical Engineering , IIT Bombay#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 Li and Ri are their respective durations corresponding to phoneme qi then ,utterance consisting of N phones can be denoted by:
Normalized durations given by:
Department of Electrical Engineering , IIT Bombay#Duration scoring (cont.) :Overall duration score given by :
Maximum duration score is 1 and minimum is 0.
Department of Electrical Engineering , IIT Bombay#Duration scoring (Results) :Speaker_1 was taken as reference and duration scores were calculated for other speakers.Speaker_1
Department of Electrical Engineering , IIT Bombay#Duration scoring (Results) :Speaker_1 Vs Speaker_2 Duration score = 0.573 (low due to differences in f, a and s duration)
Department of Electrical Engineering , IIT Bombay#Duration scoring (Results) :Speaker_1 Vs Speaker_3Duration score = 0.485 (low due to differences in f, a and s duration)
Department of Electrical Engineering , IIT Bombay#Feedback , Articulation and Duration ScoreSpeaker 2Canonical Transcription (Reference speaker)SIL f aa n d aa m ee n clt t aa l s SILSpeaker 1TranscriptionSIL f aa n d ee m ee n clt t aa l s SILFeedback
Articulation Score: 72%Duration Score: 0.573SIL f aa n d ee m ee n clt t aa l s SILDepartment of Electrical Engineering , IIT Bombay#43ReferencesStrik, H., Neri, A., and Cucchiarini, C. 2008. Speech technology for language tutoring. In Proceedings of LangTech ( Rome, Italy,February 28-29, 2008).Witt, S., and Young, S. 2000. Phone-level pronunciation scoring and assessment for interactive language learning. Speech Communication. Vol. 30, pp. 95-108, 2000.Franco, H., et al. 2000. Automatic scoring of pronunciation quality. Speech Communication. Vol. 30, pp. 83-93, 2000.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.Goronzy, S., Rapp, S., Kompe, R. 2004. Generating non-native pronunciation variants for lexicon adaption. Speech Communication. Vol. 42, pp. 109-123, 2004.Lee, K.F. 1998. Large-vocabulary speaker-independent continuous speech recognition: The SPHINX system. Ph.D. dissertation, Comput. Sci. Dep., Carnegie Mellon University.Young, S., et al. 2006. The HTK Book v3. Cambridge University, 2006. 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.Sunil K. Gupta, Ziyi Lu and Fengguang Zhao, Automatic Pronunciation Scoring for Language Learning , U.S. Patent 7,219,059, May 15, 2007.Department of Electrical Engineering , IIT Bombay#
