Speaker-Independent Word Recognition with Backpropagation Networks

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This paper presents a system that recognizes a limited vocabulary of spoken words in a speaker-independent manner. The system requires only minimal hardware support for acoustic preprocessing. In contrast to other approaches to word-level recognition, it reduces the information content of the speech signals by a compression algorithm before presenting them as inputs to a standard 3-layer backpropagation network. The network learns to recognize the utterances of the speakers in the training set, and the trained network is then used to recognize the spoken words of unknown speakers. Recognition rates of up to 91% were obtained for unknown speakers of the same sex and up to 72% for a mix of both male and female speakers. Since the training times are fast and the system is very cost effective, the approach is practically feasible for a variety of applications.