A hybrid genetic-neural front-end extension for robust speech recognition over telephone lines

Authors:
Sid-Ahmed Selouani;Habib Hamam;Douglas O'Shaughnessy
Affiliations:
Université de Moncton, Canada;Université de Moncton, Canada;INRS-Énergie-Matériaux-Télécommunications, Montréal, Canada
Venue:
NOLISP'07 Proceedings of the 2007 international conference on Advances in nonlinear speech processing
Year:
2007

Citing 2
Cited 0

Speech recognition in noisy environments: a survey

Speech Communication
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a hybrid technique combining the Karhonen-Loeve Transform (KLT), the Multilayer Perceptron (MLP) and Genetic Algorithms (GAs) to obtain less-variant Mel-frequency parameters. The advantages of such an approach are that the robustness can be reached without modifying the recognition system, and that neither assumption nor estimation of the noise are required. To evaluate the effectiveness of the proposed approach, an extensive set of continuous speech recognition experiments are carried out by using the NTIMIT telephone speech database. The results show that the proposed approach outperforms the baseline and conventional systems.