Approximation capabilities of multilayer feedforward networks
Neural Networks
Modeling time varying system using hidden control neural architecture
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Continuous speech recognition by linked predictive neural networks
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Speech recognition using demi-syllable neural prediction model
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Speech recognition using neural networks
Speech recognition using neural networks
Connectionist Speech Recognition: A Hybrid Approach
Connectionist Speech Recognition: A Hybrid Approach
Predictive Models for Sequence Modelling, Application to Speech and Character Recognition
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Large-vocabulary speaker-independent continuous speech recognition: the sphinx system
Large-vocabulary speaker-independent continuous speech recognition: the sphinx system
Challenges in adopting speech recognition
Communications of the ACM - Multimodal interfaces that flex, adapt, and persist
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
An LVQ based reference model for speaker-adaptive speech recognition
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
A discriminative neural prediction system for speech recognition
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
Some notes on nonlinearities of speech
Nonlinear Speech Modeling and Applications
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This tutorial describes a context-dependent Hidden Control Neural Network (HCNN) architecture for large vocabulary continuous speech recognition. Its basic building element, the context-dependent HCNN model, is connectionist network trained to capture dynamics of sub-word units of speech. The described HCNN model belongs to a family of Hidden Markov Model/Multi-Layer Perceptron (HMM/MLP) hybrids, usually referred to as Predictive Neural Networks [1]. The model is trained to generate continuous real-valued output vector predictions as opposed to estimate maximum a posteriori probabilities (MAP) when performing pattern classification. Explicit context-dependent modeling is introduced to refine the baseline HCNN model for continuous speech recognition. The extended HCNN system was initially evaluated on the Conference Registration Database of CMU. On the same task, the HCNN modeling yielded better generalization performance than the Linked Predictive Neural Networks (LPNN). Additionally, several optimizations were possible when implementing the HCNN system. The tutorial concludes with the discussion of future research in the area of predictive connectionist approach to speech recognition.