State-Dependent Phoneme-Based Model Merging for Dialectal Chinese Speech Recognition

Aiming at building a dialectal Chinese speech recognizer from a standard Chinese speech recognizer with a small amount of dialectal Chinese speech, a novel, simple but effective acoustic modeling method, named statedependent phoneme-based model merging (SDPBMM) method, is proposed and evaluated, where a tied-state of standard triphone(s) will be merged with a state of the dialectal monophone that is identical with the central phoneme in the triphone(s). It can be seen that the proposed method has a good performance however it will introduce a Gaussian mixtures expansion problem. To deal with it, an acoustic model distance measure, named pseudo-divergence based distance measure, is proposed based on the difference measurement of Gaussian mixture models and then implemented to downsize the model size almost without causing any performance degradation for dialectal speech. With a small amount of only 40-minute Shanghai-dialectal Chinese speech, the proposed SDPBMM achieves a significant absolute syllable error rate (SER) reduction of 5.9% for dialectal Chinese and almost no performance degradation for standard Chinese. In combination with a certain existing adaptation method, another absolute SER reduction of 1.9% can be further achieved.