Abstract

A new approach to automatic speech recognition based on independent class-conditional probability estimates in several frequency sub-bands is presented. The approach is shown to be especially applicable to environments which cause partial corruption of the frequency spectrum of the signal. Some of the issues involved in the implementation of the approach are also addressed. 1. INTRODUCTION When speech signal is partly degraded e.g. by a frequency selective noise, some part of the speech spectrum may still carry a valid information. A typical signal representation used in automatic speech recognition (ASR) consists of a series of feature vectors, each vector representing the entire short-term frequency spectrum at a given time instant. Even one or a few corrupted elements in the feature vector lead to severe degradation of the recognition performance. Earlier work by Fletcher on articulatory index [1] (review in [2]) suggests that the human auditory mechanism decodes the linguistic mes...

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