UIGHUR SPEAKER-INDEPENDENT SPEECH RECOGNITION BASED ON CDCPM

The Uighur speech recognition research is in the starting stage. Introduced in this paper is Uighur speaker independent speech recognition based on the center distance continuance probability model （CDCPM）. CDCPM describes the feature space of model by center distance normal distribution （CDN）, and simplifies and improves the HMM efficiently by getting rid of the state transition probability matrix A. A large amount of experimentation carried out with the Uighur synthetic speech database shows that numbers of state and amalgamate density for models can be adjusted adequately. When the number of model is 525, state number of model is 16, and the amalgamate density number of model is 24, the rate of first correct recognition is up to 94 76% （in set） and 97.90%（out set） on the Uighur speaker independent speech recognition. Recognition result with good performance is derived. At the same time, some problems about Uighur speech recognition research are pointed out.