Context-independent phonetic hidden Markov models for speaker-independent continuous speech recognition

Abstract

Context-dependent phone models are applied to speaker-independent continuous speech recognition and shown to be effective in this domain. Several previously proposed context-dependent models are evaluated, and two new context-dependent phonetic units are introduced: function-word-dependent phone models, which focus on the most difficult subvocabulary; and generalized triphones, which combine similar triphones on the basis of an information-theoretic measure. The subword clustering procedure used for generalized triphones can find the optimal number of models, given a fixed amount of training data. It is shown that context-dependent modeling reduces the error rate by as much as 60%.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>