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

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... proach 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. Index Terms: speech recognition, modeling unit selection, coarticulation 978-1-4244-4199-0/09/$25.00 ©2009 IEEE Authorized licensed use limited to: INSTITUTE OF AUTOMATION CAS. Downloaded on December 7, 2009 at 05:11 from IEEE Xplore. Restrictions apply.