TurnBench: A Multi-Domain Benchmark for Turn-Taking Dynamics in Spoken Dialogue

2026-08-25

AI Summary

为解决自然对话中轮流发言评估不足的问题，通过构建包含30小时手标对话的多领域基准TurnBench及标准化评估协议来改进。

Abstract

Speakers in natural conversation take turns speaking and listening, deciding in real time when to take, hold, or yield the floor. However, turn-taking evaluation remains limited due to the lack of a consistent, linguistically grounded evaluation protocol and hand-annotated data covering diverse conversation types. To address this, we present TurnBench, a multi-domain benchmark that pairs a 30-hour, hand-labeled corpus of dyadic human conversation with a standardized evaluation protocol for end-of-turn and interruption detection. We set conversation type as a controllable experimental variable, covering six distinct interaction styles, and triple-annotate each conversation. Benchmarking 14 heterogeneous turn-taking systems, we find end-of-turn recall stable across types, while interruption false positives are strongly type-dependent and concentrated in backchannel-dense interaction styles. Although in smooth floor transfers human listeners begin speaking a median 151 ms before the current turn ends, no current system performs equivalently without incurring excessive false positives. We release our corpus, a 104-hour training set, and a public leaderboard with an interactive dataset viewer at https://turnbench.sesame.com

Problem

• turn-taking dynamics
• evaluation protocol
• hand-annotated data

Innovation

• turn-taking dynamics
• multi-domain benchmark
• hand-labeled corpus
• standardized evaluation protocol
• interruption detection