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A procedure has been developed for making voiced,unvoiced,and silence classifications of speech by using a multilayer feedforward net-work.Speech signals were analyzed sequentially and a feature vector wasobtained for each segment.The feature vector served as input to a3-layer feedforward network in which voiced,unvoiced,and silence classifi-cation was made.The network had a 6-12-3 node architecture and wastrained using the generalized delta rule for back propagation of error.The performance of the network was evaluated using speech samples from3 male and 3 female speakers.A speaker-dependent classification rate of94.7% and speaker-independent classification rate of 94.3% wereobtained.It is concluded that the voiced,unvoiced,and silence classifica-tion of speech can be effectively accomplished using a multilayerfeedforward network.
A procedure has been developed for making voiced, unvoiced, and silence classifications of speech by using a multilayer feedforward net-work. Speech signals were analyzed sequentially and a feature vector wasobtained for each segment. The feature served served as input to a 3-layer feedforward network in which voiced, unvoiced, and silence classifi-cation was made. The network had a 6-12-3 node architecture and wastrained using the generalized delta rule for back propagation of error. The performance of the network was evaluated using speech samples from3 male and 3 female speakers. A speaker-dependent classification rate of 94.7% and speaker-independent classification rate of 94.3% wereobtained. It is said that the voiced, unvoiced, and silence classifica- tion of speech can be actual accomplished using a multilayer feed network.