{'fraction_non_alphanumeric': 0.020177562550443905, 'fraction_numerical': 0.003228410008071025, 'mean_word_length': 5.358974358974359, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
SNR-invariant PLDA with multiple speaker subspaces

To deal with the mismatch between the enrollment and test utterances caused by noise with different signal-to-noise ratios (SNR), we have recently proposed an SNR-invariant PLDA model for robust speaker verification. In the model, SNR-specific information were separated from speaker-specific information through marginalizing out the SNR factors during the scoring process. However, this modeling approach assumes that speaker variabilities can be captured by a single speaker subspace regardless of the noise level of the utterances. We will show in this paper that i-vectors extracted from utterances with different noise levels will shift to different regions of the i-vector space and that i-vectors extracted from utterances having similar SNR tend to cluster together. In view of this observation, we propose introducing multiple speaker subspaces to the SNR-invariance PLDA model and use multiple covariance matrices to represent SNR-dependent channel variability. Through NIST 2012 SRE, this paper demonstrates that this finer and more precise modeling of speaker and SNR variabilities leads to better performance when compared with the conventional PLDA and SNR-invariant PLDA.