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arXiv AI By Rini Sharon, Manickavela A, Kadri Hacioglu, Andreas Stolcke

Less can be More: What Aspects of Speech Drive End-of-Turn Detection

Read the original on arXiv AI →

The study investigates which speech aspects best signal the end of a speaker’s turn in conversational AI. By systematically ablating acoustic, prosodic, and semantic cues in a lightweight trimodal classifier, the authors find that combining acoustic and prosodic features yields the highest accuracy and lowest latency, achieving an utterance F1 of 0.93 with 7.8% false alarms at 400 ms median latency. Adding textual information actually increases premature detections without improving performance, and feature analysis shows prosodic cues provide the strongest class separability while text representations overlap significantly.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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