Acoustic and prosodic cues improve speech turn end detection accuracy
Less can be More: What Aspects of Speech Drive End-of-Turn Detection
Artificial IntelligenceSound
Summary
Knowing when someone finishes talking is important for smooth conversations with AI. The paper shows that listening to sound patterns and intonation helps computers better guess when a person stops speaking. Surprisingly, understanding the words doesn’t improve this guess and may actually cause errors. The authors found that focusing on how something is said, rather than what is said, leads to faster and more reliable detection of turns in conversation.
What this means in practice
- •For voice assistant developers: Build more responsive conversational agents by prioritizing acoustic and prosodic cues for faster and reliable speech turn detection.$Commercial implications: Enables creation of smoother AI assistants that better detect when users finish speaking, enhancing user experience and market competitiveness.
- •For call center technology teams: Improve call routing and human-agent handovers by using sound and intonation patterns to detect speaker turns without relying on text transcription.
Authors
Rini Sharon, Manickavela A, Kadri Hacioglu, Andreas Stolcke
Abstract
In conversational AI, detecting when a speaker has finished talking is crucial for natural turn taking. While recent work incorporates semantics, the relative contribution of different modalities remains unclear. We present a controlled ablation of acoustic, prosodic, and semantic signals for streaming end of turn detection using a lightweight trimodal classifier. Under identical training conditions, the acoustic prosodic combination achieves the best balance of accuracy and latency, achieving utterance F1 of 0.93 with 7.8% false alarms at 400ms median latency. Adding text increases premature detections without improving performance. Feature space analysis confirms that prosodic features have the strongest class separability, while text representations overlap substantially. These findings suggest that turn-taking is primarily conveyed through intonation and silence patterns rather than semantic completeness, enabling faster and more reliable systems without expensive text inference.