Maximum entropy based tone modeling for mandarin speech recognition

To explore the potential of prosody for Mandarin speech recognition, this paper addresses the tone modeling problem and its integration issue. This study adopts the maximum entropy approach to capture both acoustic and lexical characteristics of tones due to its flexibility in handling multiple interacting features. Moreover, considering the phoneme factor, besides a tone model, a phoneme dependent model is also constructed. With regard to the model integration, the presented models are integrated into the recognizer under the one-pass decoding framework, where they are used to prune the active word-final states during beam search. Experimental results on the HUB-4 evaluation material reveal the effectiveness of the presented models. They significantly improve the performance of speech recognition with 7.6% and 11.1% relative reduction of character error rate.