This paper proposes innovative multi-channel bayesian estimators in the feature-domain for robust speech recognition. Both minimum-mean-squared-error (MMSE) and maximum-a-posteriori (MAP) criteria have been explored: the related algorithms extend the multi-channel frequency-domain counterparts and generalize the single-channel feature-domain MMSE solution, recently appeared in the literature. Computer simulations conducted on a modified AURORA2 database show the efficacy of the frequency-domain multi-channel estimators when used as a pre-processing stage of a speech recognition engine, and that the proposed multi-channel MAP approach outperforms single-channel estimators by at least 3% on average.

Robust Speech Recognition Using Feature-Domain Multi-Channel Bayesian Estimators / Principi, E; Rotili, R; Cifani, S; Marinelli, Lorenzo; Squartini, S; Piazza, F.. - (2010).

Robust Speech Recognition Using Feature-Domain Multi-Channel Bayesian Estimators

PRINCIPI E;ROTILI R;CIFANI S;SQUARTINI S;F. PIAZZA
2010-01-01

Abstract

This paper proposes innovative multi-channel bayesian estimators in the feature-domain for robust speech recognition. Both minimum-mean-squared-error (MMSE) and maximum-a-posteriori (MAP) criteria have been explored: the related algorithms extend the multi-channel frequency-domain counterparts and generalize the single-channel feature-domain MMSE solution, recently appeared in the literature. Computer simulations conducted on a modified AURORA2 database show the efficacy of the frequency-domain multi-channel estimators when used as a pre-processing stage of a speech recognition engine, and that the proposed multi-channel MAP approach outperforms single-channel estimators by at least 3% on average.
2010
9781424453085
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/52324
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