Noise Robust Aurora-2 Speech Recognition Employing a Codebook-Constrained Kalman Filter Preprocessor

In this paper, a speech signal estimation framework involving Kalman filters for use as a front-end to the Aurora-2 speech recognition task is presented. Kalman-filter based speech estimation algorithms assume autoregressive (AR) models for the speech and the noise signals. In this paper, the parame...

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Bibliographic Details
Main Authors: Krishnan, V., Siniscalchi, S.M., Anderson, D.V., Clements, M.A.
Format: Conference Proceeding
Language:English
Subjects:
Automatic speech recognition
Feature extraction
Noise robustness
Signal processing
Speech enhancement
Speech processing
Speech recognition
State estimation
Time measurement
Working environment noise
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Summary:In this paper, a speech signal estimation framework involving Kalman filters for use as a front-end to the Aurora-2 speech recognition task is presented. Kalman-filter based speech estimation algorithms assume autoregressive (AR) models for the speech and the noise signals. In this paper, the parameters of the AR models are estimated using a expectation-maximization approach. The key to the success of the proposed algorithm is the constraint on the AR model parameters corresponding to the speech signal to belong to a codebook trained on AR parameters obtained from clean speech signals. Aurora-2 noise-robust speech recognition experiments are performed to demonstrate the success of the codebook-constrained Kalman filter in improving speech recognition accuracy in noisy environments. Results with both clean and multi-conditional training are provided to show the improvements in the recognition accuracy compared to the base-line system where no pre-processing is employed
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2006.1660137