A robust clustering approach to fuzzy Gaussian mixture models for speaker identification

Author

Tran, Dat ; Wagner, Michael

Author_Institution

Human-Comput. Commun. Lab., Canberra Univ., ACT, Australia

fYear

1999

fDate

36495

Firstpage

337

Lastpage

340

Abstract

The Gaussian mixture model (GMM) is a currently used method for speaker recognition. The fuzzy GMM (FGMM) proposed in previous work (D. Tran et al., 1998) is a fuzzy clustering based modification of the GMM. Although both the FGMM and the GMM are capable of achieving high identification accuracy, they have a common disadvantage in the problem of sensitivity to outliers. The paper presents an improvement for the FGMM to handle this problem. Experimental results on 16 speakers using the TI46 database are also reported

Keywords

Gaussian processes; fuzzy set theory; knowledge based systems; pattern clustering; speaker recognition; FGMM; TI46 database; fuzzy GMM; fuzzy Gaussian mixture models; fuzzy clustering based modification; identification accuracy; outliers; robust clustering approach; speaker identification; speaker recognition; Australia; Automatic speech recognition; Clustering algorithms; Databases; Iterative algorithms; Laboratories; Robustness; Speaker recognition; Speech processing; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge-Based Intelligent Information Engineering Systems, 1999. Third International Conference

Conference_Location

Adelaide, SA

Print_ISBN

0-7803-5578-4

Type

conf

DOI

10.1109/KES.1999.820192

Filename

820192