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Title: Mixture of PLDA for noise robust i-vector speaker verification
Authors: Mak, MW 
Pang, X 
Chien, JT
Issue Date: Jan-2016
Source: IEEE/ACM transactions on audio, speech, and language processing, Jan. 2016, v. 24, no. 1, 2499038, p. 130-142
Abstract: In real-world environments, noisy utterances with variable noise levels are recorded and then converted to i-vectors for cosine distance or PLDA scoring. This paper investigates the effect of noise-level variability on i-vectors. It demonstrates that noise-level variability causes the i-vectors to shift, causing the noise contaminated i-vectors to form clusters in the i-vector space. It also demonstrates that optimal subspaces for discriminating speakers are noise-level dependent. Based on these observations, this paper proposes using signal-to-noise ratio (SNR) of utterances as guidance for training mixture of PLDA models. To maximize the coordination among the PLDA models, mixtures of PLDA models are trained simultaneously via an EM algorithm using the utterances contaminated with noise at various levels. For scoring, given a test i-vector, the marginal likelihoods from individual PLDA models are linearly combined by the posterior probabilities of the test utterance's SNR. Verification scores are the ratio of the marginal likelihoods. Results based on NIST 2012 SRE suggest that the SNR-dependent mixture of PLDA is not only suitable for the situations where the test utterances exhibit a wide range of SNR, but also beneficial for the test utterances with unknown SNR distribution. Supplementary materials containing full derivations of the EM algorithms and scoring functions can be found in http://bioinfo.eie.polyu.edu.hk/mPLDA/SuppMaterials.pdf.
Keywords: I-vectors
Mixture of PLDA
Noise robustness
Probabilistic LDA
Speaker verification
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE/ACM transactions on audio, speech, and language processing 
ISSN: 2329-9290
DOI: 10.1109/TASLP.2015.2499038
Rights: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication M. Mak, X. Pang and J. Chien, "Mixture of PLDA for Noise Robust I-Vector Speaker Verification," in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 24, no. 1, pp. 130-142, Jan. 2016 is available at https://doi.org/10.1109/TASLP.2015.2499038.
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