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About this paper
Qiongqiong Wang, Kong Aik Lee
A research paper by Qiongqiong Wang and Kong Aik Lee introduces uncertainty-aware cosine scoring, which adapts standard cosine similarity to incorporate the inherent uncertainty of neural speaker embeddings. This method notably improves speaker recognition performance by an average of 8.5% in EER and 9.8% in minDCF compared to conventional cosine similarity, especially for short or degraded utterances, without sacrificing computational efficiency.
Key insights
- Uncertainty-aware embeddings (UP-Emb) themselves improve speaker representation quality, even when used with standard cosine or PLDA back-ends.
- Propagating uncertainty directly into the cosine scoring function significantly enhances the robustness and accuracy of speaker verification, bridging a gap between advanced embedding extraction and simple scoring.
- The estimated embedding uncertainty strongly correlates with utterance duration, confirming that shorter utterances yield higher uncertainty, validating the uncertainty measure.
Summary by alphaXiv.
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