MLP Based Phoneme Detectors for Speech Recognition

Samuel Thomas, Patrick Nguyen, Geoffrey Zweig, and Hynek Hermansky

Abstract

Phoneme posterior probabilities estimated using Multi-Layer Perceptrons

(MLPs) are extensively used both as acoustic scores and

features for speech recognition. In this paper we explore a different

application of these posteriors - as phonetic event detectors for

speech recognition. We show how these detectors can be built to reliably

capture phonetic events in the acoustic signal by integrating

both acoustic and phonetic information about sound classes. These

event detectors are used along with Segmental Conditional Random

Fields (SCRFs) to improve the performance of speech recognition

systems on the Broadcast News task.

Details

Publication typeInproceedings
Published inICASSP
PublisherIEEE
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