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.

Additional Metadata
Conference Proceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5)
Citation
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).