A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References
arXiv:2508.14623v2 Announce Type: replace-cross Abstract: This paper examines the implications of using the Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) as both evaluation and training objective in supervised speech separation, when the training references contain noise, as is the case with the de facto benchmark WSJ0-2Mix. A derivation of the SI-SDR with noisy references reveals that noise limits the achievable SI-SDR, or leads to undesired noise in the separated outputs. To address this, a method is proposed to enhance references and augment the mixtures with WHAM!, aiming to train mo
The paper addresses a known challenge in speech separation research, specifically the limitations of SI-SDR when reference data contains noise, which is pertinent as AI models for audio processing mature and move towards real-world applications.
Improving the robustness and accuracy of speech separation models through better training objectives and data preparation directly impacts the performance of voice assistants, telemedicine, and secure communication systems.
By proposing a method to enhance references and augment mixtures, this research paves the way for more resilient and effective speech separation AI, potentially leading to clearer audio in challenging environments.
- · AI researchers
- · Speech technology companies
- · Users of voice assistants
- · Telecommunication providers
Speech separation models will become more reliable and performant in noisy conditions.
Improved speech separation could enable more sophisticated and accurate AI applications in fields like healthcare and security.
As AI better distinguishes individual voices, privacy concerns related to audio surveillance might intensify, alongside opportunities for enhanced user authentication.
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Read at arXiv cs.AI