Attention-Enhanced Joint Speech Enhancement and Speaker Verification Accuracy on the RECOVER Benchmark Under Varying Noise

Recent advancements in speaker verification techniques show promise, but their performance often deteriorates significantly in challenging acoustic environments. Although speech enhancement methods can improve perceived audio quality, they may unintentionally distort speaker-specific information, which can affect verification accuracy. This problem has become more noticeable with the increasing use of generative deep neural networks (DNNs) for speech enhancement. While these networks can produce intelligible speech even in conditions of very low signal-to-noise ratio (SNR), they may also sever

Research goal: What is the effect of incorporating attention mechanisms into joint speech enhancement and speaker verification models on the speaker verification accuracy in the RECOVER benchmark under varying noise conditions?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.