Digit-specific HMM i-vectors with uncertainty normalization reach 1.52% male and 1.77% female EER on RSR2015 part III using only that corpus and simple cosine scoring.
A novel scheme for speaker recognition using a phonetically-aware deep neural network,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2019 2verdicts
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An iterative audio-visual approach for speaker diarisation in real-world meetings that enrolls speaker models via correspondence and outperforms prior methods on the AMI corpus.
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Speaker Recognition with Random Digit Strings Using Uncertainty Normalized HMM-based i-vectors
Digit-specific HMM i-vectors with uncertainty normalization reach 1.52% male and 1.77% female EER on RSR2015 part III using only that corpus and simple cosine scoring.
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Who said that?: Audio-visual speaker diarisation of real-world meetings
An iterative audio-visual approach for speaker diarisation in real-world meetings that enrolls speaker models via correspondence and outperforms prior methods on the AMI corpus.