Title: Improved Syllable Based Acoustic Modeling by Inter-Syllable Transition Model for Continuous Chinese Speech Recognition
Abstract: Accurately modeling the acoustic variabilities caused by coarticulation is important in continuous speech recognition. Recent research indicates that syllable units do better in modeling intra-syllable co-articulation effect than sub-syllable units. However, most continuous Mandarin speech recognition systems use context dependent phones or initial/finals (IFs) as the basic acoustic unit because it is difficult to collect sufficient data to train longer units. Here we present a syllable based approach which includes two steps. Firstly, context independent syllable based acoustic models are trained, and the models are initialized by intra-syllable IFs based diphones to solve the problem of training data sparsity. Secondly, we capture the inter-syllable co-articulation effect by incorporating inter-syllable transition models into the recognition system. Experiment results show that the acoustic model based on the presented approach is effective in improving the recognition performance.
Publication Year: 2009
Publication Date: 2009-11-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 5
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