Naohiro Tawara

Sound extraction accuracy improves dramatically when leveraging the hierarchical relationships of an ontology, allowing for flexible querying across various sound categories.

SphereVBx achieves superior clustering accuracy in speaker diarization while significantly simplifying the process, making it a game-changer for EEND-VC applications.

LLMs can outperform humans in predicting the next speaker in meetings, even without audio or visual data.

Achieving 70% of the benefits of ideal tight-label training, this method transforms how speaker diarization models handle loose annotations.