Integrating articulatory information into stochastic segment models for continuous mandarin speech recognition.

This paper proposed a framework which attempted to incorporate articulatory information into the stochastic segment model based mandarin speech recognition system. According to the characteristics of the stochastic segment model, it used hierarchical artificial neural network to obtain the posteriors of speech signal belonging to the phonemes. Then, the posteriors were integrated into the stochastic segment model system in the first search process. Experiments conducted on "863-test" set show that about 5.93% relative improvement can be achieved in the recognition accuracy. Thus, potential of the method is demonstrated. [ABSTRACT FROM AUTHOR]