Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35635
Title: Fusion of SNR-dependent PLDA models for noise robust speaker verification
Authors: Pang, X
Mak, MW 
Keywords: i-Vectors
LDA
NIST 2012 SRE
Noise robustness
Probabilistic
Speaker verification
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings of the 9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014, 2014, 6936593, p. 619-623 How to cite?
Abstract: The i-vector representation and probabilistic linear discriminant analysis (PLDA) have shown state-of-the-art performance in many speaker verification systems. However, in real-world environments, additive and convolutive noise cause mismatches between training and recognition conditions, degrading the performance. In this paper, a fusion system that combines a multi-condition PLDA model and a mixture of SNR-dependent PLDA models is proposed to make the verification system noise robust. The SNR of test utterances is used to determine the best SNR-dependent PLDA model to score against the target-speaker's i-vectors. The performance of the fusion system is demonstrated on NIST 2012 SRE. Results show that the SNR-dependent PLDA models can reduce EER and that the fusion system is more robust than the conventional i-vector/PLDA systems under noisy conditions. It is also found that the SNR-dependent PLDA models are insensitive to Z-norm parameters.
Description: 9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014, 12-14 September 2014
URI: http://hdl.handle.net/10397/35635
ISBN: 9781479942206
DOI: 10.1109/ISCSLP.2014.6936593
Appears in Collections:Conference Paper

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