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ISIS and NISIS: New bilingual dual-channel speech corpora for robust speaker recognition

Pal, A; Bose, S; Mitra, M; Roy, S; (2012) ISIS and NISIS: New bilingual dual-channel speech corpora for robust speaker recognition. In: Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012. (pp. pp. 936-939). Green open access

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Abstract

It is standard practice to use benchmark datasets for comparing meaningfully the performance of a number of competing speaker identification systems. Generally, such datasets consist of speech recordings from different speakers made at a single point of time, typically in the same language. That is, the training and test sets both consist of speech recorded at the same point of time in the same language over the same recording channel. This is generally not the case in real-life applications. In this paper, we introduce a new database consisting of speech recordings of 105 speakers, made over four sessions, in two languages and simultaneously over two channels. This database provides scope for experimentation regarding loss in efficiency due to possible mismatch in language, channel and recording session. Results of experiments with MFCC-based GMM speaker models are presented to highlight the need of such benchmark datasets for identifying robust speaker identification systems.

Type: Proceedings paper
Title: ISIS and NISIS: New bilingual dual-channel speech corpora for robust speaker recognition
ISBN-13: 9781601322258
Open access status: An open access version is available from UCL Discovery
Publisher version: https://worldacademyofscience.org/worldcomp12/ws/c...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/1539709
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