Speech recognition in noisy environments: a survey
Speech Communication
Data-driven environmental compensation for speech recognition: a unified approach
Speech Communication
Predictive model-based compensation schemes for robust speech recognition
Speech Communication - Special issue on robust speech recognition
Acoustical and Environmental Robustness in Automatic Speech Recognition
Acoustical and Environmental Robustness in Automatic Speech Recognition
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
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Our work presents a novel data driven compensation technique that modifies on-line the incoming spectral representation of degraded speech to approximate the features of high quality speech used to train a classifier. We apply the Bayesian inference framework to the degraded spectral coefficients based on modeling clean speech linear-spectrum with appropriate non-Gaussian distributions that allow maximum a-posteriori (MAP) closed form solution to be set. MAP solution leads to a soft threshold function applied and adapted to the spectral characteristics and noise variance of each spectral band. We perform extensive evaluation of our algorithm against white and coloured Gaussian noise in the context of Automatic Speech Recognition (ASR), and demonstrate its robustness in adverse conditions. The enhancement process comes at little to no extra computational overhead, thus achieving real time, on line performance.