Pronunciation Modeling With Reduced Confusion for Mandarin Chinese Using a Three-Stage Framework

Multiple-pronunciation dictionaries have been found to be useful in pronunciation modeling for speech recognition. However, the extra pronunciation variants added in the dictionary inevitably increase the confusion among different words during recognition, and consequently limit the achievable improvements in the recognition performance. This paper proposes a three-stage framework for Mandarin Chinese to construct automatically the multiple-pronunciation dictionary while reducing the possible confusion caused. The proposed framework includes pronunciation generation (Stage 1), ranking (Stage 2) and pruning (Stage3). New measures of confusability for multiple-pronunciation dictionaries were developed and shown to have a very strong correlation with recognition performance. With the proposed framework, it was shown that the confusability as measured can be reduced and recognition performance improved stage by stage. All of the above findings were verified by a series of experiments performed on both planned (LDC HUB-4NE) and spontaneous (LDC CALLHOME) Mandarin Chinese speech corpora