Minimum Hypothesis Phone Error as a Decoding Method for Speech Recognition

Haihua Xu, Daniel Povey, Jie Zhu, and Guanyong Wu

Abstract

In this paper we show how methods for approximating phone

error as normally used for Minimum Phone Error (MPE) discriminative

training, can be used instead as a decoding criterion

for lattice rescoring. This is an alternative to Confusion Networks

(CN) which are commonly used in speech recognition.

The standard (Maximum A Posteriori) decoding approach is a

Minimum Bayes Risk estimate with respect to the Sentence Error

Rate (SER); however, we are typically more interested in

the Word Error Rate (WER). Methods such as CN and our proposed

Minimum Hypothesis Phone Error (MHPE) aim to get

closer to minimizing the expected WER. Based on preliminary

experiments we find that our approach gives more improvement

than CN, and is conceptually simpler.

Details

Publication typeInproceedings
Published inInterspeech 2009
PublisherInternational Speech Communication Association
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