SNR-Invariant PLDA Modeling in Nonparametric Subspace for Robust Speaker Verification

While i-vector/PLDA framework has achieved great success, its performance still degrades dramatically under noisy conditions. To compensate for the variability of i-vectors caused by different levels of background noise, this paper proposes an SNR-invariant PLDA framework for robust speaker verification. First, nonparametric feature analysis (NFA) is employed to suppress intra-speaker variation and emphasize the discriminative information inherited in the boundaries between speakers in the i-vector space. Then, in the NFA-projected subspace, SNR-invariant PLDA is applied to separate the SNR-specific information from speaker-specific information using an identity factor and an SNR factor. Accordingly, a projected i-vector in the NFA subspace can be represented as a linear combination of three components: speaker, SNR, and channel. During verification, the variability due to SNR and channels are integrated out when computing the marginal likelihood ratio. Experiments based on NIST 2012 SRE show that the proposed framework achieves superior performance when compared with the conventional PLDA and SNR-dependent mixture of PLDA.

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