| Waibel, A., Hampshire II, J. B. (1991) Neural Network Applications to Speech, in P. Antognetti & V. Milutinovi'c (Eds). Neural Networks: Concepts, Applications, and Implementations 1, 54--76, Prentice Hall. 24 |
....network will nonlinearly depend on a combination of the previous network state and the current input [7] A comparison of different recurrent architectures and learning rules is given in [20] Furui [15] discusses various methods for speech recognition. Lippmann [27] and Waibel and Hampshire [52] give two reviews on using neural networks for speech recognition. Early work used recurrent neural networks for representation of temporal context but after the introduction of time delay neural networks by Waibel at al. 51] feedforward networks are also used for phoneme recognition. Lee and ....
Waibel, A., Hampshire II, J. B. (1991) Neural Network Applications to Speech, in P. Antognetti & V. Milutinovi'c (Eds). Neural Networks: Concepts, Applications, and Implementations 1, 54--76, Prentice Hall. 24
.... of digits [12] A comparison of k nn [11] back propagation, and radial basis functions [29] in recognizing handwritten digits is given by Lee [24] A recent review of the task and several neural and conventional approaches are given by Senior [35] Lippmann [25] and Waibel and Hampshire [38] give two reviews on using neural networks for speech recognition. Early work used recurrent neural networks for representation of temporal context but after the introduction of Time Delay Neural Networks by Waibel at al. 37] a feed forward network can also be used for phoneme recognition. Lee ....
Waibel A, Hampshire II JB. Neural Network Applications to Speech. In P. Antognetti & V. Milutinovi'c (Eds). Neural Networks: Concepts, Applications, and Implementations 1 (pp. 54--76), Prentice Hall, 1991.
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