Speech recognition in noisy environments using first-order vector Taylor series
Speech Communication
Speech recognition in noisy environments
Speech recognition in noisy environments
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
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
Model-Based Feature Compensation for Robust Speech Recognition
Fundamenta Informaticae
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In this paper, we develop a two-domain feature compensation approach to the log-filterbank and log-energy features for reducing the effects of noise. The environment model is approximated by statistical linear approximation (SLA) function. The cepstral and log-energy feature vectors of the clean speech are trained by using the Self-Organizing Map (SOM) neural network with the assumption that the speech can be well represented as multivariate diagonal Gaussian mixtures model (GMM). With the effective training of clean speech and environment model approximation, noise statistics is well estimated using batch-EM algorithm in a maximum likelihood (ML) sense. Experiments in the large vocabulary speaker-independent continuous speech recognition demonstrate that this approach exhibits a noticeable performance.