Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Robust speech recognition using neural networks and hidden markov models: adaptations using nonlinear transformations
Environment-independent continuous speech recognition using neural networks and hidden Markov models
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
Telephone speech recognition using neural networks and hidden Markov models
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
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When there is a mismatch between training and testing environments, statistical pattern classification methods may suffer from severe degradation in their performance because the parameters in the classifiers do not represent the testing data well. The mismatch is typically due to the interference or noises from operating environments. In this paper, a neural network based transformation approach is studied to handle the distribution mismatches between training and testing data. The probability density functions of the statistical classifiers are used as the objective function of the neural network. The neural network maximizes the likelihood of the data from a testing environment, and allows global optimization of the network when used with the statistical pattern classifiers. The proposed approach is applied to the area of automatic speech recognition to recognize noisy distant-talking speech and it reduces the error rate by 52.9%.