Phoneme environment clustering for speech recognition

A general principle is proposed to solve problems in context-dependent phoneme segment (or subword unit) based speech recognition, namely, how to choose the set of units and how to estimate the context effect missing in the training data. A phoneme environment clustering algorithm, which automatically selects an optimal set of allophones and estimates the missing context, is presented. This algorithm additionally gives the means to analyze coarticulation effects automatically and quantitatively. The problem is formulated as a clustering technique in phoneme environment space to approximate the mapping function from phoneme environment space to phoneme pattern space by a limited number of centroid patterns based on a distortion measure defined on the phoneme pattern space. The algorithm is tested for phoneme recognition and word recognition, and results are discussed