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How to integrate automatic speech recognition (ASR) into CALL applications Helmer Strik Department of Linguistics Centre for Language and Speech Technology.

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Presentation on theme: "How to integrate automatic speech recognition (ASR) into CALL applications Helmer Strik Department of Linguistics Centre for Language and Speech Technology."— Presentation transcript:

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2 How to integrate automatic speech recognition (ASR) into CALL applications Helmer Strik Department of Linguistics Centre for Language and Speech Technology (CLST) Radboud University Nijmegen, The Netherlands Radboud University Nijmegen

3 LESLLA, Antwerpen, Overview Introduction ASR: automatic speech recognition ASR-based tutoring ASR-based CALL ASR-based literacy training Conclusions

4 Radboud University Nijmegen LESLLA, Antwerpen, Introduction Students who receive 1-on-1 instruction perform as well as the top two percent of students who receive traditional classroom instruction [Bloom 1984] A human tutor for every student is not feasible  computer tutors For language learning: CALL Many text-based CALL systems Include speech  speech-based CALL system

5 Radboud University Nijmegen LESLLA, Antwerpen, Speech inside Many applications with ‘speech’: Screen readers [#] Reading pen Mobile phone: photo + OCR + TTS Some also (useful) for CALL [#]

6 Radboud University Nijmegen LESLLA, Antwerpen, Speech inside (cont’d) Many applications with ‘speech’ Screen readers, reading pen, etc. Some also (useful) for CALL However, usually the learner can only listen (TTS: text-to-speech) or, also speak, but … no assessment, or the learner has to carry out the assessment, e.g. by comparing with examples  use ASR / speech technology Is it feasible?

7 Radboud University Nijmegen LESLLA, Antwerpen, ASR: automatic speech recognition What is ASR? Speech to text conversion Applications: Dictation Command and control Spoken dialogue systems (information) etc. ASR is not flawless, and it will probably never be esp. for non-native speech Note: this is not even the case for humans!

8 Radboud University Nijmegen LESLLA, Antwerpen, Speech Recognition cgn2-s vb nn mii

9 Radboud University Nijmegen LESLLA, Antwerpen, ASR-based tutoring ITS: Intelligent Tutoring Systems Spoken dialogue system for learning Subject matter: math, physics, etc. Examples: ITSPOKE, Univ. of Pittsburgh, Litman et al. Topic: Physics SCoT, Stanford Univ., Peters et al. Topic (SCoT-DC): shipboard damage control Communicate with speech the subject matter doesn’t have to be speech

10 Radboud University Nijmegen LESLLA, Antwerpen, ASR-based CALL The subject matter is speech (language) Late 1990’s: 1998: STiLL, Marholmen (Sweden); 1 st time the CALL and Speech communities met 1999: Special Issue of CALICO, 'Tutors that Listen‘, focusing on ASR (mainly ‘discrete ASR’)

11 Radboud University Nijmegen LESLLA, Antwerpen, ASR-based literacy training What has been done?  Reading tutors (the learner reads, not the PC): Listen, CMU, Pittsburgh; Mostow et al. (1994) STAR system, UK; Russel et al. (1996) SPACE, KU Leuven; Van hamme, Duchateau, et al. … and many others [#]  FtL: Foundations to Literacy, Boulder; Cole, Wise, et al.

12 Radboud University Nijmegen LESLLA, Antwerpen, ASR-based literacy training Foundations to Literacy Interactive Books Teach fluent reading & comprehension Foundational Skills Tutors Teach underlying reading skills Phonics

13 Radboud University Nijmegen LESLLA, Antwerpen, ASR-based literacy training (cont’d) What has been done? Reading tutors: Listen, CMU, Pittsburgh; Mostow et al. (1994) STAR system, UK; Russel et al. (1996) SPACE, KU Leuven; Van hamme, Duchateau, et al. …, and many others FtL: Foundations to Literacy, Boulder; Cole, Wise, et al. Mostly for children And for adults? What is needed? What is possible, and what is not? …

14 Radboud University Nijmegen LESLLA, Antwerpen, ASR-based CALL ASR is not flawless, and it will probably never be esp. for non-native speech Be aware of what is (not) possible with ASR technology Problematic issues and possible solutions: Noise, esp. background speech  min., head-sets Disfluencies  min., improve autom. handling Non-native pronunciation Recognizing utterances  utterance verification Detect pronunciation errors  classifiers

15 Radboud University Nijmegen LESLLA, Antwerpen, ASR-based CALL Our research: Non-natives Assessment of oral proficiency Dutch-CAPT – pronunciation oASR / UV – Utterance Verification oPED – Pronunciation Error Detection DISCO – pronunciation, morphology, syntax TST-AAP People with speech disability for training & as communication aid (AAC) ASR for dysarthric speech EST: E-learning based Speech Therapy

16 Radboud University Nijmegen LESLLA, Antwerpen, ASR-based CALL Project Dutch-CAPT (Computer Assisted Pronuciation Training)

17 Radboud University Nijmegen LESLLA, Antwerpen,

18 Radboud University Nijmegen LESLLA, Antwerpen, ASR-based CALL (cont’d) Project Dutch-CAPT (CAPT: Computer Assisted Pronuciation Training) Exp. group: used the Dutch-CAPT system 2 control groups: didn’t use Dutch-CAPT The reduction in the number of pronunciation errors made was significantly larger for the exp. group, Training: 4 weeks x 1 session of 30’ – 60’

19 Radboud University Nijmegen LESLLA, Antwerpen, ASR-based CALL (cont’d) ASR is not flawless, and it will probably never be esp. for non-native speech Be aware of what is (not) possible with ASR technology Problematic issues and possible solutions: Noise, esp. background speech  min., head-sets Disfluencies  min., improve autom. handling Non-native pronunciation Recognizing utterances  utterance verification Detect pronunciation errors  classifiers Mix of expertise needed: ASR techn., L-acq., pedagogy, design, …

20 Radboud University Nijmegen LESLLA, Antwerpen, ASR-based literacy training Demonstration project TST-AAP Existing course Add speech technology: Detect whether words & sounds were pronounced (correctly)

21 Radboud University Nijmegen LESLLA, Antwerpen, ASR-based literacy training Listening; PC: produces speech Text-To-Speech (TTS); quality good enough? Recorded speech, concatenation Speaking;PC: recognizes speech Phonics (see FtL) PC: Recognize words, utterances: CMs for Utt. Ver. PC: Recognize sounds: CMs for Phon. Ver. (contrasts) Reading (reading tutors) PC: Recognize words, utterances PC: Pointer in the text (‘track’ the reader) PC: Help when encountering problems PC: Change tempo  read faster

22 Radboud University Nijmegen LESLLA, Antwerpen, ASR-based CALL Advantages of using speech (vs. writing) Self-explanation Extra information: Prosody (stress, accent) Emotions Confidence Other useful techniques: VTH [#]

23 Radboud University Nijmegen LESLLA, Antwerpen, Conclusions ASR is not flawless ASR-based tutoring is possible (restricted domain) general topics; ITS: ITSPOKE, SCoT CALL; many systems: non-natives, disabled, etc. Literacy training So far mainly for children And for adults !? Needed Mix of expertise: techn., L-acq., pedagogy, design, … Improved ASR, speech technology Projects, funds

24 Radboud University Nijmegen LESLLA, Antwerpen, Questions? Why are there so few ASR-based CALL / literacy applications for adults? What are, in this context, important differences between children & adults? What is needed? Listening; PC: produces speech Speaking;PC: recognizes speech Phonics Reading (reading tutors) What else?

25 Radboud University Nijmegen LESLLA, Antwerpen, Questions? Why are there so few ASR-based CALL / literacy applications for adults? What are, in this context, important differences between children & adults? What is needed? Listening; PC: produces speech Speaking;PC: recognizes speech Phonics Reading (reading tutors) What else?

26 Radboud University Nijmegen LESLLA, Antwerpen,


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