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.
Original language | English |
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Pages (from-to) | 633-648 |
Number of pages | 16 |
Journal | International Journal of Speech Technology |
Volume | 18 |
Issue number | 4 |
DOIs | |
Publication status | Published - 1 Dec 2015 |
Keywords
- Fusion
- i-Vectors
- NIST 2012 SRE
- Noise robustness
- Probabilistic LDA
- Speaker verification
ASJC Scopus subject areas
- Software
- Language and Linguistics
- Human-Computer Interaction
- Linguistics and Language
- Computer Vision and Pattern Recognition