Context-dependent hidden control neutral network architecture for continuous speech recognition

The authors present a context-dependent, phoneme and function work based, hidden control neutral network (HCNN-CDF) architecture for continuous speech recognition. The system can be seen as a large vocabulary extension of the word-based HCNN system proposed by E. Levin (1990). Two main extensions towards a large vocabulary speech recognition system are presented and discussed, i.e., the context-dependent HCNN phoneme model and the context-dependent HCNN function word model. When compared to the linked predictive neural network (LPNN) system of, J. Tebelskis (1990) significant savings in resource requirements and computational load for the HCNN-CDF implementation can be achieved. In speaker-dependent recognition experiments with perplexity 111, the current versions of the LPNN and HCNN-CDF systems achieve 60% and 75% word recognition accuracies, respectively