We present a context-dependent, phoneme and function word based, Hidden Control Neural Network (HCNN) architecture for continuous speech recognition. The system can be seen as a large vocabulary extension of the word-based HCNN system proposed by Levin [Levin90]. Initially, we analysed the context-independent HCNN modeling principle in the framework of the Linked Predictive Neural Network speech recognition system [Tebelskis91] and found that it results in a 6% increase of the word recognition accuracy at perplexity 402. Significant savings in the resource requirements and computational load for the HCNN implementation can be achieved. In speaker-dependent recognition experiments with perplexity 111, the current versions of the LPNN and HCNN systems achieve 60% and 75% word recognition accuracy, respectively. Keywords: Automatic Speech Recognition, Hid- den Control Neural Network, Large vocabulary recognition, Context-dependent modeling, Function-word modeling.
Bibliographic reference. Petek, Bojan / Waibel, Alex H. / Tebelskis, Joseph M. (1991): "Integrated phoneme-function word architecture of hidden control neural networks for continuous speech recognition", In EUROSPEECH-1991, 1407-1410.