Title

Speaker Identification Based On Adaptive Discriminative Vector Quantisation

Abstract

A novel adaptive discriminative vector quantisation technique for speaker identification (ADVQSI) is introduced. In the training mode of ADVQSI, for each speaker, the speech feature vector space is divided into a number of subspaces. The feature space segmentation is based on the difference between the probability distribution of the speech feature vectors from each speaker and that from all speakers in the speaker identification (SI) group. Then, an optimal discriminative weight, which represents the subspace's role in SI, is calculated for each subspace of each speaker by employing adaptive techniques. The largest template differences between speakers in the SI group are achieved by using optimal discriminative weights. In the testing mode of ADVQSI, discriminative weighted average vector quantisation (VQ) distortions are used for SI decisions. The performance of ADVQSI is analysed and tested experimentally. The experimental results confirm the performance improvement employing the proposed technique in comparison with existing VQ techniques for SI and recently reported discriminative VQ techniques for SI (DVQSI). © 2006 The Institution of Engineering and Technology.

Publication Date

12-6-2006

Publication Title

IEE Proceedings: Vision, Image and Signal Processing

Volume

153

Issue

6

Number of Pages

754-760

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1049/ip-vis:20050074

Socpus ID

33751534908 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33751534908

This document is currently not available here.

Share

COinS