Robust speech/non-speech detection using LDA applied to MFCC

Author

Martin, Arnaud ; Charlet, Delphine ; Mauuary, Laurent

Author_Institution

France Telecom R&D, Lannion, France

Volume

1

fYear

2001

fDate

2001

Firstpage

237

Abstract

In speech recognition, speech/non-speech detection must be robust to,noise. In the paper, a method for speech/non-speech detection using a linear discriminant analysis (LDA) applied to mel frequency cepstrum coefficients (MFCC) is presented. The energy is the most discriminant parameter between noise and speech. But with this single parameter, the speech/non-speech detection system detects too many noise segments. The LDA applied to MFCC and the associated test reduces the detection of noise segments. This new algorithm is compared to the one based on signal to noise ratio (Mauuary and Monne, 1993)

Keywords

covariance matrices; finite automata; speech recognition; statistical analysis; linear discriminant analysis; mel frequency cepstrum coefficients; noise segments; nonspeech detection; signal to noise ratio; speech detection; very noisy environment; Cepstrum; Linear discriminant analysis; Mel frequency cepstral coefficient; Noise reduction; Noise robustness; Signal to noise ratio; Speech analysis; Speech enhancement; Speech recognition; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on

Conference_Location

Salt Lake City, UT

ISSN

1520-6149

Print_ISBN

0-7803-7041-4

Type

conf

DOI

10.1109/ICASSP.2001.940811

Filename

940811