Special Issue on Artificial Intelligence Techniques for Pervasive Computing; Guest Editors: Ioannis Vlahavas and Nick BassiliadesNo Access

SPEECH ENHANCEMENT FOR ROBUST SPEECH RECOGNITION IN MOTORCYCLE ENVIRONMENT

IOSIF MPORAS

Artificial Intelligence Group, Wire Communications Laboratory, Department of Electrical and Computer Engineering, University of Patras, Rion-Patras, 26500, Greece

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TODOR GANCHEV

Artificial Intelligence Group, Wire Communications Laboratory, Department of Electrical and Computer Engineering, University of Patras, Rion-Patras, 26500, Greece

Corresponding author.

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OTILIA KOCSIS

Artificial Intelligence Group, Wire Communications Laboratory, Department of Electrical and Computer Engineering, University of Patras, Rion-Patras, 26500, Greece

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, and

NIKOS FAKOTAKIS

Artificial Intelligence Group, Wire Communications Laboratory, Department of Electrical and Computer Engineering, University of Patras, Rion-Patras, 26500, Greece

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https://doi.org/10.1142/S0218213010000091Cited by:4 (Source: Crossref)

Abstract

In the present work, we investigate the performance of a number of traditional and recent speech enhancement algorithms in the adverse non-stationary conditions, which are distinctive for motorcycles on the move. The performance of these algorithms is ranked in terms of the improvement they contribute to the speech recognition accuracy, when compared to the baseline performance, i.e. without speech enhancement. The experiments on the MoveOn motorcycle speech and noise database indicated that there is no equivalence between the ranking of algorithms based on the human perception of speech quality and the speech recognition performance. The Multi-band spectral subtraction method was observed to lead to the highest speech recognition performance.

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