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

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1 Author(s)
Lee, K.-F. ; Sch. of Comput. Sci., Carnegie-Mellon Univ., Pittsburgh, PA, USA

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%

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Acoustics, Speech and Signal Processing, IEEE Transactions on  (Volume:38 ,  Issue: 4 )