PHMM based asynchronous acoustic model for Chinese large vocabulary continuous speech recognition

In this paper, we presented an asynchronous multiple stream based Chinese tonal acoustic modeling framework. In this framework, toneless phonetic units and tones are modeled separately with different acoustic features. During the training and decoding process, a set of models are coupled together with a product hidden Markov models (PHMM) to represent whole tonal phonetic units. Through this, a compound context dependent tonal model can be generated from a few simple models. Experiments show that such model scheme generates more compact and accurate model presentation and brings improvement on the performance for large vocabulary speech recognition tasks.