Example-Based Speech Recognition Using Formulaic Phrases

Christopher Watkins, Stephen Cox

Research output: Contribution to conferencePaper

Abstract

In this paper, we describe the design of an ASR system that is based on identifying and extracting formulaic phrases from a corpus and then, rather than building statistical models of them, performing example-based recognition of these phrases. We describe a method for combining formulaic phrases into a bigram language model that results in a 13% decrease in WER on a monophone HMM recogniser over the baseline. We show that using this model with phrase templates in the example-based recogniser gives a significant improvement in WER compared to word templates, but performance still falls short of the HMM recogniser. We also describe an LDA decision tree classifier that reduces the search space of the DTW decoder by 40% while at the same time decreasing WER.
Original languageEnglish
Pages3043-3046
Number of pages4
Publication statusPublished - Sep 2009
Event10th Annual Conference of the International Speech Communication Association (INTERSPEECH) - Brighton, United Kingdom
Duration: 6 Sep 200910 Sep 2009

Conference

Conference10th Annual Conference of the International Speech Communication Association (INTERSPEECH)
CountryUnited Kingdom
CityBrighton
Period6/09/0910/09/09

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