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In this talk, we explore advancements in computational models for speech quality assessment. Self-supervised learning models have emerged as powerful front-ends, outperforming supervised-only models. However, their large size renders them impractical for production tasks. We discuss strategies to distill self-supervised learning-based models into more compact forms using unlabeled data, achieving significant size reduction while maintaining an advantage over supervised-only models.

Speakers: Benjamin Stahl
Host: Hannes Gamper