Abstract
Statistical language models can play an important role in continuous speech recognition, but their performance is often unstable because of the training data sparsity. This paper proposes a statistical language modeling method, where the contribution of the language model is limited by the acoustic matching result and the N-gram probability distribution is modified referring to the length of the silence duration between adjacent syllables. Besides, the paper proposes a powerful single-state hidden Markov model (HMM) to model various kinds of silence segments.
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