Application of a multilayer feedforward network to voiced-unvoiced-silence classifications of speech

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.