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Noise robust speaker verification via the fusion of SNR-independent and SNR-dependent PLDA

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Abstract

While i-vectors with probabilistic linear discriminant analysis (PLDA) can achieve state-of-the-art performance in speaker verification, the mismatch caused by acoustic noise remains a key factor affecting system performance. In this paper, a fusion system that combines a multi-condition signal-to-noise ratio (SNR)-independent PLDA model and a mixture of SNR-dependent PLDA models is proposed to make speaker verification systems more noise robust. First, the whole range of SNR that a verification system is expected to operate is divided into several narrow ranges. Then, a set of SNR-dependent PLDA models, one for each narrow SNR range, are trained. During verification, the SNR of the test utterance is used to determine which of the SNR-dependent PLDA models is used for scoring. To further enhance performance, the SNR-dependent and SNR-independent models are fused using linear and logistic regression fusion. The performance of the fusion system and the SNR-dependent system is evaluated on the NIST 2012 speaker recognition evaluation for both noisy and clean conditions. Results show that a mixture of SNR-dependent PLDA models perform better in both clean and noisy conditions. It was also found that the fusion system is more robust than the conventional i-vector/PLDA systems under noisy conditions.

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Notes

  1. http://dnt.kr.hsnr.de/download.html.

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Acknowledgments

This work was in part supported by The Hong Kong Research Grant Council (Grant Nos. PolyU 152117/14E and PolyU 152068/15E) and The Hong Kong Polytechnic University (Grant No. 4-ZZCX).

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Authors and Affiliations

  1. Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Xiaomin Pang & Man-Wai Mak

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  1. Xiaomin Pang
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  2. Man-Wai Mak
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Correspondence to Man-Wai Mak.

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Pang, X., Mak, MW. Noise robust speaker verification via the fusion of SNR-independent and SNR-dependent PLDA. Int J Speech Technol 18, 633–648 (2015). https://doi.org/10.1007/s10772-015-9310-8

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