Jacobian Approach to Fast Acoustic Model Adaptation
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Speech recognition in noisy environments
Speech recognition in noisy environments
A vector Taylor series approach for environment-independent speech recognition
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
PCA-PMC: a novel use of a priori knowledge for fast parallel model combination
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Two-domain feature compensation for robust speech recognition
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Environment compensation based on maximum a posteriori estimation for improved speech recognition
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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This paper proposes a novel robust speech recognition approach based on the model-based feature compensation. The approach combines the GMM-based feature compensation and the HMM-based feature compensation together and employs the multiple recognition passes to achieve the best performance. In the initial recognition procedure, the GMM-based feature compensation approach is employed to give better clean model and noise model. Then we further refine these models by employing the HMM-based feature compensation approach. The statistical model of the clean speech and the noise is combined by using vector Taylor series (VTS) approximation. The experimental results show that the novel approach makes a significant improvement compared to the GMM-based feature compensation and the HMM-based feature compensation without any compensation in the initial pass.