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Speech prediction using recurrent neural networks

Speech prediction using recurrent neural networks

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Published

Complementary work to that carried out by Chu and Bose (1998) into speech signal prediction using feedforward neural networks is reported. Results of prediction performance and network efficiency using a recurrent neural network (RNN) are presented and compared to those obtained using a multilayer perceptron (MLP) and a linear predictor. An RNN structure is described which has much better performance than that of an MLP at almost the same network efficiency.

Inspec keywords: speech processing; feedforward neural nets; recurrent neural nets; prediction theory

Other keywords: recurrent neural network; feedforward neural network; multilayer perceptron; linear predictor; nonlinear dynamical system; speech signal prediction

Subjects: Speech recognition and synthesis; Speech recognition; Neural computing techniques

References

    1. 1)
      • W.C. Chu , N.K. Bose . Speech signal prediction using feedforward neural network. Electron. Lett. , 10 , 999 - 1001
    2. 2)
      • Tishby, N.: `A dynamical systems approach to speech processing', Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, ICASSP-90, April 1990, p. 365–368.
    3. 3)
      • R.J. Williams , D. Zipser . A learning algorithm for continually running fully recurrent neural networks. Neural Comput. , 270 - 280
    4. 4)
      • G. Kubin , W.B. Kleijn , K.K. Paliwal . (1995) Nonlinear processing of speech, Speech coding and synthesis.
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