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Open Problems in Speech Recognition Nelson Morgan, EECS and ICSI.

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Presentation on theme: "Open Problems in Speech Recognition Nelson Morgan, EECS and ICSI."— Presentation transcript:

1 Open Problems in Speech Recognition Nelson Morgan, EECS and ICSI

2 ICSI and EECS International Computer Science Institute Nonprofit, closely affiliated with UCB-EECS: - faculty (e.g., Morgan, Feldman) - Board (Berlekamp, Karp, Malik) - students (PhD, MS) Focus areas in speech,language,theory, internet research; CITRIS involvement

3 A working speech recognizer (circa 1920)

4 A working speech recognizer (circa 2002)

5 Current Applications Toys Telephone queries (operator/touch tone replacement) Voice dialing (for cell phones) Dictation (esp. for specific domains)

6 Major Reasons for Success Late 60’s statistical methodology (HMMs, developed for cryptography) applied to speech in 70’s and 80’s Moore’s Law + engineering refinements to HMM training/recognition (1986-now) Normalization approaches (mean norms, RASTA filtering, vocal tract length approx)

7 Two examples of things that helped RASTA: 2% digit error -> 60% for different phone system; down to 3% using RASTA; now used for voice dialing in millions of cell phones Vocal tract length normalization: 1 parameter for each speaker, significant effect on errors; now used in all large research systems

8 Major Technical Challenges Speaker variability for fluent/conversational (pronunciation, rate, overlaps)  25-40%error on conversations Acoustic variability for general environments (noise, reverb, talker movement)  3-10%error on read digits (vs <1% in clean conditions)

9 Modern ASR Systems From 50,000 ft, all ASR systems the same: - compute local spectral envelope - determine likelihoods of speech sounds - search for most likely HMMs Spectral envelope distorted by many things - Alternatives often are bad fits to the statistical models

10 Pronunciation Lexicon Signal Processing Phonetic Probability Estimator Decoder (word search) Words Speech Grammar ASR in Brief

11 ASR is half-deaf Phonetic classification very poor Success due to constraints (domain, speaker, noise-canceling mic, etc) These constraints can mask the underlying weakness of the technology

12 Rethinking Acoustic Processing for ASR Escape dependence on spectral envelope Use multiple front ends across time/freq Modify statistical models to accommodate new front ends Design optimal combination schemes for multiple models

13 The DARPA (IAO) “EARS” Program New 5 year program to radically reduce errors in conversational speech-to-text Two components: - Rich Transcription (large reductions in error rate, improvements in readability and portability to new languages) - Novel Approaches (radical changes)

14 EARS: Effective Affordable Reusable Speech-to-text Rich Transcription: 4 teams - SRI/ICSI/UW - BBN/U.Pitt/UW/LIMSI - Cambridge U. - IBM Novel Approaches: 2 teams - ICSI/SRI/UW/OGI/Columbia/IDIAP - Microsoft

15 time Novel Approach 1: Pushing the Envelope (aside) Problem: Spectral envelope is a fragile information carrier estimate of sound identity information fusion 10 ms OLD PROPOSED Solution: Probabilities from multiple time-frequency patches i-th estimate up to 1s k-th estimate n-th estimate estimate of sound identity

16 Novel Approach 2: Beyond Frames… Solution: Advanced features require advanced models, not limited by fixed-frame-rate paradigm OLD PROPOSED conventional HMM short-term features Problem: Features & models interact, new features may require different models advanced features multi-rate / dynamic scale classifier

17 Other speech-to-text projects Dialog systems: DARPA Communicator/Symphony, German SmartKom Noise/reverberation for cell phone, military environments: DARPA SPINE program, various European projects (EU, ETSI) Recognition/retrieval/summarization for multiparty meetings: Swiss IM2, EU m4, ICSI/UW/SRI/Columbia NSF-ITR

18 Resource generation from Berkeley researchers gmtk - a new graphical model toolkit specialized for speech (extension of 2 PhD theses, Bilmes [UW] and Zweig [IBM]) - Publicly available speech/neural network software (RASTA, speech neural network training system) Soon: a “meeting data” corpus

19 Campus interaction Within EECS (CIS): - Feldman (also ICSI), NLU - Jordan and Russell, machine learning Linguists: - Ohala, phonology - Fillmore(ICSI), semantic lexicography

20 Natural Speech + Language Projects at ICSI/EECS Berkeley Restaurant Project (BeRP) - online stochastic context free grammar probabilities with natural mixed initiative SmartKom - tourist information query system w/American pronunciations of German place names

21 Summary Progress in speech recognition research led to working systems in particular domains Performance still severely limited for conversational speech, noisy/reverberant conditions We and others are working to transcend these limitations with novel approaches


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