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Enhancement systems can significantly alter ASR outcomes, but the best choice varies by task and context, challenging the notion of a one-size-fits-all solution.
A single-layer speech enhancement model outperforms naive architectures and achieves competitive quality with a significant speedup through progressive knowledge distillation.
A single universal speech enhancement model can effectively adapt to multiple latency requirements without sacrificing performance, challenging the need for specialized models.
Voice recordings can reveal the oscillating states of Recurrent Respiratory Papillomatosis, providing a unique longitudinal perspective on a rare laryngeal disease.
Gaze is a surprisingly effective cue for resolving the cocktail party problem, boosting audio-visual speech enhancement by over 23% in SI-SDR.
Time-shifted anechoic speech beats early reflections as a training target for universal speech enhancement, leading to better perceptual quality and ASR performance.
