Context-Dependent Phonetic Hidden Markov Models for Speaker-Independent Continuous Speech Recognition

Abstract

The effectiveness of context-dependent phone modeling for speaker-dependent continuous speech recognition has recently been demonstrated. In this study, we apply context-dependent phone models to speaker-independent continuous speech recognition, and show that they are equally effective in this domain. In addition to evaluating several previously proposed context-dependent models, we also introduce two new context-dependent phonetic units: 1) function-word-dependent phone models, which focus on the most difficult subvocabulary, and 2) generalized triphones, which combine similar triphones together based on 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. We demonstrate that context-dependent modeling reduces the error rate by as much as 60%.