Links Between Markov Models and Multilayer Perceptrons
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel distributed processing: explorations in the microstructure, vol. 2: psychological and biological models
Readings in speech recognition
Readings in speech recognition
Modeling time varying system using hidden control neural architecture
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Large-vocabulary speaker-independent continuous speech recognition: the sphinx system
Large-vocabulary speaker-independent continuous speech recognition: the sphinx system
Large vocabulary speech recognition using neural prediction model
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Continuous speech recognition using linked predictive neural networks
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Multipredictor modelling with application to chaotic signals
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: digital speech processing - Volume III
Performance through consistency: connectionist large vocabulary continuous speech recognition
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
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We present a context-dependent, phoneme and function word based, Hidden Control Neural Network (HCNN -CDF) architecture for continuous speech recognition. The system can be seen as a large vocabulary extension of the wordbased HCNN system proposed by Levin [7]. 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 [13]. 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.