Parameterization of speech signals for robust voice recognition

Author

Zouhir, Youssef ; Ouni, Kais

Author_Institution

Higher Sch. of Technol. & Comput. Sci., Univ. of Carthage, Tunis, Tunisia

fYear

2014

Firstpage

1

Lastpage

4

Abstract

In this paper, we propose a speech parameterization technique based on a compressive Gammachirp filterbank. This filterbank represents a reliable model of the cochlear auditory filter and provides a good approximation of their spectral and selective behaviour. The recognition performance of our technique is tested on isolated-words extracted from the TIMIT database. The adopted speech recognition system is the HTK.3.4.1 platform based on Hidden Markov Models with Gaussian-Mixture densities. The evaluation results showed that the proposed technique gives better recognition rate compared to conventional techniques: PLP (Perceptual Linear Prediction) and LPCC (Linear Prediction Cepstral Coefficient).

Keywords

Gaussian processes; audio databases; channel bank filters; compressed sensing; hidden Markov models; mixture models; speech recognition; Gaussian mixture densities; HTK.3.4.1 platform; PLP; TIMIT database; cochlear auditory filter; compressive Gammachirp filterbank; hidden Markov models; linear prediction cepstral coefficient; perceptual linear prediction; speech parameterization technique; speech recognition system; speech signals; voice recognition; Filter banks; Markov processes; Physiology; Reconnaissance; Speech; Speech processing; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Sciences and Technologies in Maghreb (CISTEM), 2014 International Conference on

Type

conf

DOI

10.1109/CISTEM.2014.7076915

Filename

7076915