title: A bio-inspired feature extraction for robust speech recognition.
abstract: In this paper, a feature extraction method for robust speech recognition in noisy environments is proposed. The proposed method is motivated by a biologically inspired auditory model which simulates the outer/middle ear filtering by a low-pass filter and the spectral behaviour of the cochlea by the Gammachirp auditory filterbank (GcFB). The speech recognition performance of our method is tested on speech signals corrupted by real-world noises. The evaluation results show that the proposed method gives better recognition rates compared to the classic techniques such as Perceptual Linear Prediction (PLP), Linear Predictive Coding (LPC), Linear Prediction Cepstral coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC). The used recognition system is based on the Hidden Markov Models with continuous Gaussian Mixture densities (HMM-GM).
authors.affiliation: Research Unit: Signals and Mechatronic Systems, SMS, Higher School of Technology and Computer Science (ESTI), University of Carthage, Carthage, Tunisia.
authors.forename: Youssef
authors.initials: Y
authors.lastname: Zouhir
authors.affiliation: Research Unit: Signals and Mechatronic Systems, SMS, Higher School of Technology and Computer Science (ESTI), University of Carthage, Carthage, Tunisia.
authors.forename: Kaïs
authors.initials: K
authors.lastname: Ouni
keywords: Auditory filter model; Feature extraction; Hidden Markov Models; Noisy speech recognition