Feature smoothing and frame reduction for speaker recognition

Conference proceedings article

Authors/Editors

Strategic Research Themes

No matching items found.

Publication Details

Author list: Nuratch S., Boonpramuk P., Wutiwiwatchai C.

Publisher: Hindawi

Publication year: 2010

Start page: 311

End page: 314

Number of pages: 4

ISBN: 9780769542881

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-79551560988&doi=10.1109%2fIALP.2010.49&partnerID=40&md5=9c699986e7d51fd94d78696e260e1a0a

Languages: English-Great Britain (EN-GB)

View on publisher site

Abstract

This paper presents a new technique for smoothing and reducing speech feature vectors for speaker recognition using an adaptive weighted-sum algorithm, aims at reducing computation time and increasing the recognition performance. The proposed technique is based on a three-frame sliding window. Each step of window sliding, three feature frames in the window are used to compute weight values based on feature Euclidean distances. The weight values are applied to original MFCC feature vectors to construct smoothed feature vectors. Simultaneously, the number of smoothed vectors is reduced from the original vectors. The smoothed and reduced feature vectors are applied on an SVM speaker recognition system with GMM supervectors. The NIST Speaker Recognition Evaluation 2006 core-test is used in evaluation. Experiment results show that our approach outperforms the baseline system using conventional RASTA filtered MFCC feature vectors. ฉ 2010 IEEE.

Keywords

Feature Reduction, Speaker recognition