A general framework of feature extraction: application to speaker recognition

Author

Liu, Chi-shi

Author_Institution

Telecom Lab., MOTC, Taiwan

Volume

2

fYear

1996

fDate

7-10 May 1996

Firstpage

669

Abstract

Extracting a good feature set is important to pattern recognition. A new formulation of integrating the feature extraction into the model training is proposed. The intraframe weighting, the interframe weighting and the feature reduction schemes can be obtained from this new formulation. According to the dependence of the class model parameters, three types of feature extraction are derived. Some experiments for the speaker recognition application are given to show the effectiveness of the new proposed feature extraction method

Keywords

feature extraction; maximum likelihood estimation; speaker recognition; experiments; feature extraction; feature reduction; feature set; interframe weighting; intraframe weighting; maximum likelihood criterion; minimum classification error; model parameters; model training; pattern recognition; speaker recognition; Cepstrum; Feature extraction; Gratings; Hidden Markov models; Linear predictive coding; Noise reduction; Pattern recognition; Speaker recognition; Speech processing; Telecommunications;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on

Conference_Location

Atlanta, GA

ISSN

1520-6149

Print_ISBN

0-7803-3192-3

Type

conf

DOI

10.1109/ICASSP.1996.543209

Filename

543209