Speaker Identification Using Data-Driven Score Classification-Reference-Cited by-同舟云学术

Speaker Identification Using Data-Driven Score Classification

Published:2016-06-01 Issue:2 Volume:21 Page:55-63
ISSN:2300-8709
Container-title:Image Processing & Communications
language:en
Short-container-title:

Author:

Gan Hock¹,Mporas Iosif¹,Safavi Saeid¹,Sotudeh Reza¹

Affiliation:

1. School of Engineering and Technology University of Hertfordshire

Abstract

Abstract We present a comparative evaluation of different classification algorithms for a fusion engine that is used in a speaker identity selection task. The fusion engine combines the scores from a number of classifiers, which uses the GMM-UBM approach to match speaker identity. The performances of the evaluated classification algorithms were examined in both the text-dependent and text-independent operation modes. The experimental results indicated a significant improvement in terms of speaker identification accuracy, which was approximately 7% and 14.5% for the text-dependent and the text-independent scenarios, respectively. We suggest the use of fusion with a discriminative algorithm such as a Support Vector Machine in a real-world speaker identification application where the text-independent scenario predominates based on the findings.

Publisher

Walter de Gruyter GmbH

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