Soft Margin Feature Extraction for Automatic Speech Recognition

Jinyu Li and Chin-Hui Lee

Abstract

We propose a new discriminative learning framework, called

soft margin feature extraction (SMFE), for jointly optimizing

the parameters of transformation matrix for feature extraction

and of hidden Markov models (HMMs) for acoustic

modeling. SMFE extends our previous work of soft margin

estimation (SME) to feature extraction. Tested on the

TIDIGITS connected digit recognition task, the proposed

approach achieves a string accuracy of 99.61%, much better

than our previously reported SME results. To our knowledge,

this is the first study on applying the margin-based method in

joint optimization of feature extraction and acoustic modeling.

The excellent performance of SMFE demonstrates the success

of soft margin based method, which targets to obtain both

high accuracy and good model generalization.

Details

Publication typeInproceedings
Published inProc. Interspeech
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