Automatically correcting bias in speaker recognition systems

Yosef A. Solewicz, Moshe Koppel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In this paper we present a general machine learning framework for score bias reduction and analysis in Speaker recognition systems. The general principle is to learn a meta-system using recognition systems' errors, given the training and testing conditions in which they occurred. In the context of speaker recognition, the proposed method is able to reduce the bias introduced in scores due to a variety of factors such as channel mismatch, additive noise, gender mismatch, different speaking styles, etc. Moreover, this framework enables a deep understanding of the origins of score bias in any system, which will support an optimized system redesign. Preliminary results obtained with several state-of-the-art systems showed considerable improvement in original performance, in addition to identifying sources of system bias.

Original languageEnglish
Title of host publicationProceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
PublisherIEEE Computer Society
Pages186-191
Number of pages6
ISBN (Print)1424406560, 9781424406562
DOIs
StatePublished - 2006
Event2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006 - Maynooth, Ireland
Duration: 6 Sep 20068 Sep 2006

Publication series

NameProceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006

Conference

Conference2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
Country/TerritoryIreland
CityMaynooth
Period6/09/068/09/06

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