Knowing the non-target speakers: The effect of the i-vector population for PLDA training in speaker recognition

Inspired by the NIST SRE-2012 evaluation conditions we train the PLDA classifier in an i-vector speaker recognition system with different speaker populations, either including or excluding the target speakers in the evaluation. Including the target speakers in the PLDA training is always beneficial...

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Bibliographic Details
Published in2013 IEEE International Conference on Acoustics, Speech and Signal Processing pp. 6778 - 6782
Main Authors van Leeuwen, David A., Saeidi, Rahim
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2013
Subjects
Calibration
Data models
i-vector
NIST
Noise
noise robustness
PLDA
Speaker recognition
Speech
Training
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Summary:Inspired by the NIST SRE-2012 evaluation conditions we train the PLDA classifier in an i-vector speaker recognition system with different speaker populations, either including or excluding the target speakers in the evaluation. Including the target speakers in the PLDA training is always beneficial w.r.t. completely excluding them-which is the normal situation in pre-2012 SRE protocols-even in the P known = 0 evaluation condition. However, adding other speakers than just the targets speakers can slightly increase performance. We also investigated the effect of adding i-vectors extracted from segments with added noise in the PLDA training. This generally makes the system more robust to noise in the test segments, and doesn't hurt performance in the clean condition. The paper further details the 'simple to compound' log-likelihood-ratio conversion necessary for SRE-2012 style calibration.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2013.6638974