Fuzzy Vector Quantization for speaker recognition under limited data conditions

Author

Jayanna, H.S. ; Prasanna, S. R Mahadeva

Author_Institution

Dept. of Electron. & Commun. Eng., Indian Inst. of Technol. Guwahati, Guwahati

fYear

2008

fDate

19-21 Nov. 2008

Firstpage

1

Lastpage

4

Abstract

This work focuses on the task of speaker recognition under limited data conditions. In case of limited data, the amount of available training and testing data will be few seconds. Under such conditions the conventional classifiers will have very few feature vectors for modelling. This work performs an experimental evaluation of three simple modelling techniques namely, direct template matching (DTM), crisp vector quantization (CVQ) and fuzzy vector quantization (FVQ). Among these FVQ shows significant improved performance compared to DTM and CVQ. For about 3 s of training and testing data the performance for DTM, CVQ and FVQ are 76.67, 73.33, and 86.67, respectively, for a set of first 30 speakers taken from the YOHO database.

Keywords

speaker recognition; vector quantisation; crisp vector quantization; direct template matching; fuzzy vector quantization; limited data conditions; speaker recognition; Biometrics; Data engineering; Databases; Performance evaluation; Speaker recognition; Speech recognition; Speech synthesis; Testing; Vector quantization; Web and internet services; CVQ and FVQ; DTM; limited data; speaker recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON 2008 - 2008 IEEE Region 10 Conference

Conference_Location

Hyderabad

Print_ISBN

978-1-4244-2408-5

Electronic_ISBN

978-1-4244-2409-2

Type

conf

DOI

10.1109/TENCON.2008.4766453

Filename

4766453