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A Tutorial on Pronunciation Modeling for Large Vocabulary Speech Recognition

A Tutorial on Pronunciation Modeling for Large Vocabulary Speech Recognition

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A Tutorial on Pronunciation Modeling for Large Vocabulary Speech Recognition

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  1. A Tutorial on Pronunciation Modeling for Large Vocabulary Speech Recognition Dr. Eric Fosler-Lussier Presentation for CiS 788

  2. Overview • Our task: moving from “read speech recognition” to recognizing spontaneous conversational speech • Two basic approaches for modeling pronunciationvariation • Encoding linguistic knowledge to pre-specify possiblealternative pronunciations of words • Deriving alternatives directly from a pronunciation corpus. • Purposes of this tutorial • Explain basic linguistic concepts in phonetics and phonology • Outline several pronunciation modeling strategies • Summarize promising recent research directions.

  3. Pronunciations & Pronunciation Modeling

  4. Pronunciations & Pronunciation Modeling • Why sub-word units? • Data sparseness at word level • Intermediate level allows extensible vocabulary • Why phone(me)s? • Available dictionaries/orthographies assume this unit • Research suggests humans use this unit • Phone inventory more manageable than syllables, etc. (in e.g., English)

  5. Statistical Underpinnings for Pronunciation Modeling • In the whole-word approach, we could find the most likely utterance (word-string) M* given the perceived signal: M* =

  6. Statistical Underpinnings for Pronunciation Modeling • With independence assumptions, we can use the following approximation: • Argmax P(M|X)

  7. Statistical Underpinnings for Pronunciation Modeling • PA(X|Q): the acoustic model • continuous sound (vector)s to discrete phone (state)s • Analogous to “categorical perception” in human hearing • PQ(Q|M): the pronunciation model • Probability of phone states given words • Also includes context-dependence & duration models • PL(M): the language model • The prior probability of word sequences

  8. Statistical Underpinnings for Pronunciation Modeling The three models working in sequence:

  9. Linguistic Formalisms & Pronunciation Variation • Phones & Phonemes • (Articulatory) Features • Phonological Rules • Finite State Transducers

  10. Linguistic Formalisms & Pronunciation Variation • Phones & Phonemes • Phones: Types of (uttered) segments • E.g., [p] unaspirated voiceless labial stop [spik] • Vs. [ph] aspirated voiceless labial stop [phik] • Phonemes: Mental abstractions of phones • /p/ in speak = /p/ in peak to naïve speakers • ARPABET: between phones & phonemes • SAMPAbet: closer to phones, but not perfect…

  11. Selected Consonants (arpa) tS chin tSIn (ch) dZ gin dZIn (jh) T thin TIn (th) D this DIs (dh) Z measure "mEZ@` (zh) N thing TIN (ng) j yacht jAt (y) 4 butter bV4@` (dx) Selected Vowels (arpa) { pat p{t (ae) A pot pAt (aa) V cut kVt (uh) ! U put pUt (uh) ! aI rise raIz (ay) 3` furs f3`z (er) @ allow @laU (ax) @` corner kOrn@` (axr) SAMPA for American English

  12. Linguistic Formalisms & Pronunciation Variation • (Articulatory) Features • Describe where (place) and how (manner) a sound is made, and whether it is voiced. • Typical features (dimensions) for vowels include height, backness, & roundness • (Acoustic) Features • Vowel features actually correlate better with formants than with actual tongue position

  13. From Hume-O’Haire & Winters (2001)

  14. Linguistic Formalisms & Pronunciation Variation • Phonological Rules • Used to classify, explain, and predict phonetic alternations in related words: write (t) vs. writer (dx) • May also be useful for capturing differences in speech mode (e.g., dialect, register, rate) • Example: flapping in American English

  15. Linguistic Formalisms & Pronunciation Variation • Finite State Transducers • (Same example transducer as on Tuesday)

  16. Linguistic Formalisms & Pronunciation Variation • Useful properties of FSTs • Invertible (thus usable in both production & recognition) • Learnable (Oncina, Garcia, & Vidal 1993, Gildea & Jurafsky 1996) • Composable • Compatible with HMMs

  17. ASR Models: Predicting Variation in Pronunciations • Knowledge-Based Approaches • Hand-Crafted Dictionaries • Letter to Sound Rules • Phonological Rules • Data-Driven Approaches • Baseform Learning • Learning Pronunciation Rules

  18. ASR Models: Predicting Variation in Pronunciations • Hand-Crafted Dictionaries • E.g., CMUdict, Pronlex for American English • The most readily available starting point • Limitations: • Generally only one or two pronunciations per word • Does not reflect fast speech, multi-word context • May not contain e.g., proper names, acronyms • Time-consuming to build for new languages

  19. ASR Models: Predicting Variation in Pronunciations • Letter to Sound Rules • In English, used to supplement dictionaries • In e.g., Spanish, may be enough by themselves • Can be learned (e.g. by DTs, ANNs) • Hard-to-catch Exceptions: • Compound-words, acronyms, etc. • Loan words, foreign words • Proper names (Brands, people, places)

  20. ASR Models: Predicting Variation in Pronunciations • Phonological Rules • Useful for modeling e.g., fast speech, likely non-canonical pronunciations • Can provide basis for speaker-adaptation • Limitations: • Requires labeled corpus to learn rule probabilities • May over-generalize, creating spurious homophones • (Pruning minimizes this)

  21. Examples of Fast-Speech Rules

  22. ASR Models: Predicting Variation in Pronunciations • Automatic Baseform Learning 1) Use ASR with “dummy” dictionary to find “surface” phone sequences of an utterance 2) Find canonical pronunciation of utterance (e.g., by forced-Viterbi) 3) Align these two (w/ dynamic programming) 4) Record “surface pronunciations” of words

  23. ASR Models: Predicting Variation in Pronunciations • Limitations of Baseform Learning • Limited to single-word learning • Ignores multi-word phrases, cross word-boundary effects (e.g., Did you  “didja”) • Misses generalizations across words (e.g., learns flapping separately for each word)

  24. ASR Models: Predicting Variation in Pronunciations • Learning Pronunciation Rules • Each word has a canonical pronunciation c1 c2 …cj…cn. • Each phone cj in a word can be pronounced by some sj. • Set of surface pronunciations S: {Si = si1, …, sin} • Taking canonical tri-phone and last surface phone into account, the probability of a given Si can be estimated:

  25. ASR Models: Predicting Variation in Pronunciations • (Machine) Learning Pronunciation Rules • Typical ML techniques apply: CART, ANNs, etc. • Using features (pre-specified or learned) helps • Brill-type rules (e.g., Yang & Martens 2000): • A  B // C __ D with P(B|A,C,D) positive rule • A  not B // C __ D with 1 - P(B|A,C,D) neg. rule (Note: equivalent to Two-level rule types 1 & 4)

  26. ASR Models: Predicting Variation in Pronunciations • Pruning Learned Rules & Pronunciations • Vary # of allowed pronunciations by word-frequency E.g., f (count(w)) = k log(count(w)) • Use probability threshold for candidate pronunciations • Absolute cutoff • “Relmax” (relative to maximum) cutoff • Use acoustic confidence C(pj,wi) as measure

  27. Online Transformation-Based Pronunciation Modeling • In theory, a dynamic dictionary could halve error-rates • Using an “oracle dictionary” for each utterance in switchboard reduces error by 43% • Using e.g., multi-word context, hidden speaking-mode states may capture some of this. • Actual results less dramatic, of course!

  28. Online Transformation-Based Pronunciation Modeling

  29. Five Problems Yet to Be Solved • Confusability and Discriminability • Hard Decisions • Consistency • Information Structure • Moving Beyond Phones as Basic Units

  30. Five Problems Yet to Be Solved • Confusability and Discriminability • New pronunciations can create homophones not only with other words, but with parts of words. • Few exact metrics exist to measure confusion

  31. Five Problems Yet to Be Solved • Hard Decisions • Forced-Viterbi throws away good, but “second-best” representations. • N-best would avoid this (Mokbel and Jouvet), but problematic for large-vocabulary • DTs also introduce hard decisions and data-splitting

  32. Five Problems Yet to Be Solved • Consistency • Current ASR works word-by-word w/o picking up on long-term patterns (e.g., stretches of fast speech, consistent patterns like dialect, speaker) • Hidden speech-mode variable helps, but data is perhaps too sparse for dialect-dependent states.

  33. Five Problems Yet to Be Solved • Information Structure • Language is about the message! • Hence, not all words are pronounced equal • Confounding variables: • Prosody & intonation (emphasis, de-accenting) • Position of word in utterance (beginning or end) • Given vs. new information; Topic/focus, etc. • First-time use vs. repetitions of a word

  34. Five Problems Yet to Be Solved • Moving Beyond Phones as Basic Units • Other types of units • “Fenones” • Hybrid phones [x+y] for //x///y/ rules • Detecting (changes in) distinctive features • E.g., [ax]  {[+voicing,+nasality], [+voicing,+nasality,+back], [+voicing,+back], …} • (cf. Autosegmental & Non-linear phonology?)

  35. Conclusions • An ideal model would: • Be dynamic and adaptive in dictionary use • Integrate knowledge of previously heard pronunciation patterns from that speaker • Incorporate higher-level factors (e.g., speaking rate, semantics of the message) to predict changes from the canonical pronunciation • (Perhaps) operate on a sub-phonetic level, too.