Open Access

On the Use of Evolutionary Algorithms to Improve the Robustness of Continuous Speech Recognition Systems in Adverse Conditions

EURASIP Journal on Advances in Signal Processing20032003:468432

DOI: 10.1155/S1110865703302070

Received: 14 June 2002

Published: 21 July 2003


Limiting the decrease in performance due to acoustic environment changes remains a major challenge for continuous speech recognition (CSR) systems. We propose a novel approach which combines the Karhunen-Loève transform (KLT) in the mel-frequency domain with a genetic algorithm (GA) to enhance the data representing corrupted speech. The idea consists of projecting noisy speech parameters onto the space generated by the genetically optimized principal axis issued from the KLT. The enhanced parameters increase the recognition rate for highly interfering noise environments. The proposed hybrid technique, when included in the front-end of an HTK-based CSR system, outperforms that of the conventional recognition process in severe interfering car noise environments for a wide range of signal-to-noise ratios (SNRs) varying from 16 dB to dB. We also showed the effectiveness of the KLT-GA method in recognizing speech subject to telephone channel degradations.


speech recognition genetic algorithms Karhunen-Loève transform hidden Markov models robustness

Authors’ Affiliations

Secteur Gestion de l'Information, Université de Moncton
INRS-Energie-Matériaux-Télécommunications, Université du Québec


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