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

• Main Authors: Sid-Ahmed Selouani, Douglas O'Shaughnessy
• Format: Article
• Language: English
• Published: SpringerOpen 2003-07-01
• Series: EURASIP Journal on Advances in Signal Processing
• Subjects: speech recognition; genetic algorithms; Karhunen-Loève transform; hidden Markov models; robustness.
• Online Access: http://dx.doi.org/10.1155/S1110865703302070

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.