Context-Dependent HMM Models for Continuous Mandarin Speech Recognition
In this thesis, the context-dependent HMM model for continuous Mandarin speech recognition system was studied. The initial-final recognition units were used in this thesis. The state splitting was used to find the context-dependent models instead of the model splitting. The state-splitting was accomplished by using acoustic decision tree method. The effectiveness of the state-splitting context-dependent HMM recognition system was confirmed by simulation on the MAT(Mandarin Across Taiwan)speech database. A HMM recognizer with 1900 state models were constructed in our study. The base-syllable recognition rate of the proposed system was 70.1% comparing with HMM using 100 final-dependent initials and 39 CI finals, 5.4% improvement was achieved.
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