Continuous Speech Recognition by Linked Predictive Neural Networks

Part of Advances in Neural Information Processing Systems 3 (NIPS 1990)

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Joe Tebelskis, Alex Waibel, Bojan Petek, Otto Schmidbauer


We present a large vocabulary, continuous speech recognition system based on Linked Predictive Neural Networks (LPNN's). The system uses neu(cid:173) ral networks as predictors of speech frames, yielding distortion measures which are used by the One Stage DTW algorithm to perform continuous speech recognition. The system, already deployed in a Speech to Speech Translation system, currently achieves 95%, 58%, and 39% word accuracy on tasks with perplexity 5, 111, and 402 respectively, outperforming sev(cid:173) eral simple HMMs that we tested. We also found that the accuracy and speed of the LPNN can be slightly improved by the judicious use of hidden control inputs. We conclude by discussing the strengths and weaknesses of the predictive approach.