AUTOMATED SPEECH RECOGNITION USING A PLURALITY OF DIFFERENT MULTILAYER PERCEPTION STRUCTURES TO MODEL A PLURALITY OF DISTINCT PHONEME CATEGORIES

For speech recognition systems a method for modeling context-dependent phonetic categories using artificial neural nets has been described. First, linguistically motivated context-clustering is employed to reduce the number of context-dependent categories. Second, phone-specific MLP structures are used where the number of outputs in each MLP is based on the number of left and right contexts occurring in a training database. The structure of each MLP can be automatically determined using the cascade-correlation learning algorithm.