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IITG-indigo system for NIST 2016 SRE challenge

Kumar, N and Das, RK and Jelil, S and Dhanush, BK and Kashyap, H and Murty, KSR and Ganapathy, S and Sinha, R and Prasanna, SRM (2017) IITG-indigo system for NIST 2016 SRE challenge. In: 18th Annual Conference of the International Speech Communication Association, 20 August 2017, Stockholm, pp. 2859-2863.

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Official URL: https://doi.org/10.21437/Interspeech.2017-1307


This paper describes the speaker verification (SV) system submitted to the NIST 2016 speaker recognition evaluation (SRE) challenge by Indian Institute of Technology Guwahati (IITG) under the fixed training condition task. Various SV systems are developed following the idea-level collaboration with two other Indian institutions. Unlike the previous SREs, this time the focus was on developing SV system using non-target language speech data and a small amount unlabeled data from target language/ dialects. For addressing these novel challenges, we tried exploring the fusion of systems created using different features, data conditioning, and classifiers. On NIST 2016 SRE evaluation data, the presented fused system resulted in actual detection cost function (actDCF) and equal error rate (EER) of 0:81 and 12:91, respectively. Post-evaluation, we explored a recently proposed pairwise support vector machine classifier and applied adaptive S-norm to the decision scores before fusion. With these changes, the final system achieves the actDCF and EER of 0:67 and 11:63, respectively.

Item Type: Conference Paper
Publication: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publisher: International Speech Communication Association
Additional Information: The copyright for this article belongs to International Speech Communication Association.
Keywords: Cost functions; Speech communication; Speech recognition; Support vector machines, IFCC; Indian institute of technologies; Pairwise SVM; Post evaluations; Speaker recognition evaluations; Speaker verification; Support vector machine classifiers; Training conditions, Classification (of information)
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 20 Jul 2022 06:44
Last Modified: 20 Jul 2022 06:44
URI: https://eprints.iisc.ac.in/id/eprint/74909

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