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Bayesian Learning for Models of Human Speech Perception

Bayesian Learning for Models of Human Speech Perception. Mark Hasegawa-Johnson, University of Illinois at Urbana-Champaign. 1. Review of Psychological Results. 1.A. Independence of Distinctive Feature Errors.

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Bayesian Learning for Models of Human Speech Perception

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  1. Bayesian Learning for Models of Human Speech Perception Mark Hasegawa-Johnson, University of Illinois at Urbana-Champaign

  2. 1. Review of Psychological Results

  3. 1.A. Independence of Distinctive Feature Errors put here: explanation of the Miller and Nicely experiment. Figure schematizing experimental setup? graphic

  4. Put here: Miller & Nicely confusion matrices at -6dB, -12dB. Tables.

  5. 1.B. Dependence of Distinctive Feature Acoustics • Put here: explanation of the Volaitis & Miller experiment: Spectrograms showing typical VOT of p,b,k,g? Spectrograms (4).

  6. 1.C. Perceptual Magnet Effect

  7. 1.D. Redundant Acoustic Correlates • Spectrograms demonstrating redundant acoustic correlates in production of stops. Spectrogram (1).

  8. 1.E. The Vowel Sequence Illusion • Spectrograms showing vowels and syllable rate, division of vowels into L/H and resulting syllable rate. Spectrograms (2).

  9. 2. A Mathematical Model

  10. 2.A. Syllable Detection followed by Explanation • Spectrograms showing syllable detection followed by syllable explanation. Figure includes: spectrogram (from 1.D.), “detected” landmark alignment times (powerpoint), apparent syllables (powerpoint).

  11. 2.B. Perceptual Space Encodes Distinctive Features Acoustic-to-Perceptual Map

  12. 3. A Machine Learning Model

  13. 3.A. ML Learning of an Explicit Perceptual Space • Equations

  14. 3.B. Discriminative Learning of an Implicit Perceptual Space • Equations

  15. 3.C. Bayesian Learning of Syllable Sequences: A Baum-Welch Algorithm for Irregular Sampling • Equations

  16. 3.D. SVM Learning of Syllable Sequences (in which Baum-Welch is a component) • Equations.

  17. Conclusions • Proposed: a machine learning model consistent with five psychophysical results. • Characteristics of the model: • Front-end processor detects syllable onsets, nuclei, codas (“acoustic landmarks”). • Acoustic-to-perceptual mapping (explicit or implied) classifies distinctive feature content of onset, nucleus, and coda of each syllable. • Experimental tests in progress

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