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From Lattices to Landmarks: Dictionary-Based Methods

From Lattices to Landmarks: Dictionary-Based Methods. Mark Hasegawa-Johnson WS04 Planning Meeting 4/16/04. Outline. Motivation: Mathematical/machine-learning advantages and disadvantages of binary distinctive features. Overview: Error compounding avoidance strategies

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From Lattices to Landmarks: Dictionary-Based Methods

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  1. From Lattices to Landmarks: Dictionary-Based Methods Mark Hasegawa-Johnson WS04 Planning Meeting 4/16/04

  2. Outline • Motivation: Mathematical/machine-learning advantages and disadvantages of binary distinctive features. • Overview: Error compounding avoidance strategies • Landmark-based dictionary created by parsing pronlex • Lattice pinching & word pronunciation alignment • Syllabified multi-edge pinching • Restricted application of SVMs (no experimental results yet) • Three possible integration methods (experimental results not yet integrated with #2-5)

  3. Why are we studying binary distinctive features? By focusing on binary distinction, and using regularized learners (SVMs), we can “push the limit” of classifier complexity … … in order to get high binary classification accuracy.

  4. What’s wrong with binary distinctive features? Error Propagation: Concatenating many binary decisions… … multiplies their correctness probabilities: pc(d1,…,dN)=pc(d1)pc(d2)…pc(dN) (1) (I.N.Q.A.B.A.T.: there are ways to raise pc(phone), e.g. redundant pairwise classifiers, voting, confidence weighting, conditional classifiers, etc. Let’s consider them).

  5. Error Propagation Avoidance Strategies • Many redundant pairwise classifiers, integrate by voting. Problem: very high complexity. • Word Lattice selects “most useful” SVMs: First-Pass ASR landmark based MAP Words best oh_and our Rescore/ Pick Best Parse SVMs Segment 1: onset +lateral? two syllables? Segment 2: coda +body? Segment 3: nucleus +high?

  6. A Landmark-Based Dictionary Intervocalic Glide LM: -syllabic between +sylls Phonemes (Pronlex) y uw+1 t r iy+1 split_phones Syllable Nucleus LM: +syllabic y uw+1 tcl r iy+1 Segments Boundary LM: change in sonorant or continuant look up features -syll,+sono,+cont,+blade,-ante -syll,+blade,-ante +syll,+stress,+high,+back +syll,+stress,+high,+back +-sono,+-cont,+blade,+ante -syll,-sono,-cont,+blade,+ante -+sono,-+cont,+blade,-ante -syll,+sono,+cont,+blade,-ante +syll,+stress,+high,-back +syll,+stress,+high,+back parse to find landmarks

  7. A Landmark-Based Dictionary

  8. Lattice Pinching & Word Alignment landmark based best oh_and our Pinch to the MAP path: Convert “lattice rescoring problem” into “Choose one of N” problem landmark based oh_and our best Segment 2: onset +lateral? two syllables? coda +body? Segment 3: nucleus +high? Segment 1:

  9. A Lattice Pinching Problem: Syllable CountMismatch

  10. Syllable Count Mismatch

  11. Syllabified Multi-Edge Pinching landmark based best oh_and our landmark_2 landmark_1 based oh best and our based landmark_2 landmark_1 best our and oh Segment 3: coda +body? Segment 4: nucleus +high? Segments 1&2: onset +lateral? two syllables?

  12. Example: Syllabified Lattice

  13. Example: Pinched Syllables

  14. Landmarks that Match, with theDistinctive Features that Differ

  15. Restricted application of SVMs • First: Re-align landmarks in MAP path (where is the /s/)? • … In all paths (where is the /y/)? • Second: Two syllables vs. One syllable? “Saying” vs. “Seen”? • Third: Find features of each Onset, Nucleus, and Coda. “Seen” vs. “Seemed”?

  16. Integration: a few ideas • Voting. “Correct” word is the one with most correct distinctive features. • Weighted voting: • SVM computes D(di=v|X), • MLP computes p(di=v|word), D(word|X) =S p(di=v|word) D(di=v|X) 3. Dynamically weighted voting: DBN pronunciation model.

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