DocumentCode :
3340847
Title :
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
Link To Document :
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