An important yet challenging task for neural network based speech recognizers is the effective processing of temporal information in speech signals. A high-order fully recurrent neural network is developed to effectively handle the sequential nature of speech signals and to accommodate both temporal and spectral variations. The proposed neural network has 4 layers, namely, the input layer, self organizing map, fully recurrent hidden layer and output layer. The important characteristics of the hidden neurons and the output neurons are their high-order processing feature. A 2-stage unsupervised/supervised training method is developed. The solution from unsupervised training provides a good starting point for supervised training. The proposed neural network and the training method are applied to isolated word recognition using the TI20 data.

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Conference Proceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5)
Zhang, Q.J., Wang, Fang, & Nakhla, M.S. (1995). High-order temporal neural network for word recognition. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 3343–3346).