Learning Structured Models for Phone Recognition

Learning Structured Models for Phone Recognition Slav Petrov, Adam Pauls, Dan Klein

Acoustic Modeling

Motivation • Standard acoustic models impose many structural constraints • We propose an automatic approach • Use TIMIT Dataset • MFCC features • Full covariance Gaussians (Young and Woodland, 1994)

? ? ? ? ? ? ? ? ? ? Phone Classification

Phone Classification æ

HMMs for Phone Classification

HMMs for Phone Classification Temporal Structure

Standard subphone/mixture HMM Temporal Structure Gaussian Mixtures

Our Model Standard Model Fully Connected Single Gaussians

25.6% 23.9% Hierarchical Baum-Welch Training 32.1% 28.7%

Phone Classification Results

? ? ? ? ? ? ? ? ? Phone Recognition

Standard State-Tied Acoustic Models

No more State-Tying

No more Gaussian Mixtures

Fully connected internal structure

Fully connected external structure

Refinement of the /ih/-phone

Refinement of the /l/-phone

Hierarchical Refinement Results

t-1 t t+1 t-1 t t+1 Merging • Not all phones are equally complex • Compute log likelihood loss from merging Split model Merged at one node

t-1 t t+1 t-1 t t+1 Merging Criterion

Split and Merge Results

HMM states per phone

Alignment Results

Alignment State Distribution

Inference • State sequence: d1-d6-d6-d4-ae5-ae2-ae3-ae0-d2-d2-d3-d7-d5 • Phone sequence: d- d- d-d-ae- ae- ae- ae- d- d-d- d- d • Transcription d - ae - d Viterbi Variational ???

Solution: : Posterior edge marginals Variational Inference Variational Approximation:

Phone Recognition Results

Conclusions • Minimalist, Automatic Approach • Unconstrained • Accurate • Phone Classification • Competitive with state-of-the-art discriminative methods despite being generative • Phone Recognition • Better than standard state-tied triphone models

Thank you! http://nlp.cs.berkeley.edu

Learning Structured Models for Phone Recognition

Learning Structured Models for Phone Recognition

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