Robust speaker verification with a two classifier format and feature enhancement

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

In the presence of environmental noise, speaker verification systems inevitably see a decrease in performance. This paper proposes the (1) use of two parallel classifiers, (2) feature enhancement based on blind signal-to-noise ratio (SNR) estimation and (3) fusion, to improve the performance of speaker verification systems. The two classifiers are based on Gaussian mixture models and the partial least-squares technique. Speech corrupted by additive noise at SNRs from 0 to 30 dB are used for authentication. A two-way analysis of variance validates the performance gain offered by the methods used. The outputs of the classifiers are fused together in different ways. The fusion method where the scores of the classifiers are added together is found to be the best method again using statistical analysis.

Original languageEnglish (US)
Title of host publicationIEEE International Symposium on Circuits and Systems
Subtitle of host publicationFrom Dreams to Innovation, ISCAS 2017 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467368520
DOIs
StatePublished - Sep 25 2017
Event50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 - Baltimore, United States
Duration: May 28 2017May 31 2017

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310

Other

Other50th IEEE International Symposium on Circuits and Systems, ISCAS 2017
Country/TerritoryUnited States
CityBaltimore
Period5/28/175/31/17

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Robust speaker verification with a two classifier format and feature enhancement'. Together they form a unique fingerprint.

Cite this