EURASIP Journal on Applied Signal Processing 
Volume 2003 (2003), Issue 8, Pages 814-823
doi:10.1155/S1110865703302070

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

Sid-Ahmed Selouani1 and Douglas O'Shaughnessy2

 1Secteur Gestion de l'Information, Université de Moncton, Campus de Shippagan, 218 boulevard J.-D.-Gauthier, Shippagan E8S 1P6, Nouveau-Brunswick, Canada
2INRS-Energie-Matériaux-Télécommunications, Université du Québec, 800 de la Gauchetière Ouest, place Bonaventure, , Montréal H5A 1K6, Canada
Received 14 June 2002; Revised 6 December 2002

Abstract

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 −4 dB. We also showed the effectiveness of the KLT-GA method in recognizing speech subject to telephone channel degradations.