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
K. VENKATESH, S. M. SINISCALCHI, D. V. ANDERSON, M. A. CLEMENTS (2006). Noise Robust Aurora-2 speech recognition employing a codebook-constrained Kalman filter preprocessor. In IEEE ICASSP 2006 (pp. 781-784). IEEE [10.1109/ICASSP.2006.1660137].
Noise Robust Aurora-2 speech recognition employing a codebook-constrained Kalman filter preprocessor
S. M. SINISCALCHIInvestigation
;
2006-01-01
Abstract
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 employedFile | Dimensione | Formato | |
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