Speakers In The Wild (SITW): The QUT Speaker Recognition System

Ghaemmaghami, Houman, Rahman, Md Hafizur, Himawan, Ivan, Dean, David, Kanagasundaram, Ahilan, Sridharan, Sridha, & Fookes, Clinton (2016) Speakers In The Wild (SITW): The QUT Speaker Recognition System. In Proceedings of the 17th Annual Conference of the International Speech Communication Association (ISCA), International Speech Communication Association (ISCA), San Francisco, CA, pp. 838-842.

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Abstract

This paper presents the QUT speaker recognition system, as a competing system in the Speakers In The Wild (SITW) speaker recognition challenge. Our proposed system achieved an overall ranking of second place, in the main core-core condition evaluations of the SITW challenge. This system uses an ivector/ PLDA approach, with domain adaptation and a deep neural network (DNN) trained to provide feature statistics. The statistics are accumulated by using class posteriors from the DNN, in place of GMM component posteriors in a typical GMM UBM i-vector/PLDA system. Once the statistics have been collected, the i-vector computation is carried out as in a GMM-UBM based system. We apply domain adaptation to the extracted i-vectors to ensure robustness against dataset variability, PLDA modelling is used to capture speaker and session variability in the i-vector space, and the processed i-vectors are compared using the batch likelihood ratio. The final scores are calibrated to obtain the calibrated likelihood scores, which are then used to carry out speaker recognition and evaluate the performance of the system. Finally, we explore the practical application of our system to the core-multi condition recordings of the SITW data and propose a technique for speaker recognition in recordings with multiple speakers.

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ID Code: 96310
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: speaker verification, i-vectors, dnn, The QUT Speaker Recognition System
DOI: 10.21437/Interspeech.2016-945
ISSN: 1990-9770
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Institutes > Information Security Institute
Funding:
Copyright Owner: Copyright 2016 ISCA
Deposited On: 22 Jun 2016 23:11
Last Modified: 05 Nov 2016 15:04

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