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
Two-domain feature compensation for robust speech recognition
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Non-stationary environment compensation using sequential EM algorithm for robust speech recognition
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Model-Based Feature Compensation for Robust Speech Recognition
Fundamenta Informaticae
Artificial Intelligence Review
Non-stationary environment compensation using sequential EM algorithm for robust speech recognition
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Model-Based Feature Compensation for Robust Speech Recognition
Fundamenta Informaticae
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In this paper, we describe environment compensation approach based on MAP (maximum a posteriori) estimation assuming that the noise can be modeled as a single Gaussian distribution. It employs the prior information of the noise to deal with environmental variabilities. The acoustic-distorted environment model in the cepstral domain is approximated by the truncated first-order vector Taylor series(VTS) expansion and the clean speech is trained by using Self-Organizing Map (SOM) neural network with the assumption that the speech can be well represented as the multivariate diagonal Gaussian mixtures model (GMM). With the reasonable environment model approximation and effective clustering for the clean model, the noise is well refined using batch-EM algorithm under MAP criterion. Experiment with large vocabulary speaker-independent continuous speech recognition shows that this approach achieves considerable improvement on recognition performance.