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This paper introduces DuplexAct-Bench, a comprehensive bilingual benchmark designed to evaluate full-duplex speech systems across six distinct interaction behaviors like interruption and proactive initiation. By analyzing 1,290 trials in English and Chinese, the study highlights significant gaps between semantic accuracy and behavioral timing, revealing that current AI models struggle with robust real-time participation under diverse conditions.
DuplexAct-Bench: Broadening Full-Duplex Speech Evaluation toward Proactive Interaction across Diverse Behavioral Requirements
arXiv:2609.39446v1 Announce Type: new Abstract: Existing full-duplex speech benchmarks cover only subsets of real-time interaction behaviors, often under limited contextual conditions. We introduce DuplexAct-Bench, a bilingual benchmark that systematically covers six complementary behaviors, from interruption and yielding to proactive initiation, active silence, and backchanneling, across Pre-session, In-session, and No-explicit conditions. Across 1,290 English and Chinese streaming trials, we evaluate 12 full-duplex speech systems on both Timing and Content. Results reveal substantial variation across behaviors, conditions, and systems, as well as frequent mismatches between semantic quality and behavioral timing. These findings show that current systems remain far from robustly managing when, whether, and how to participate as real-time interaction unfolds. Project page: https://alitaxky.icu/DuplexAct-Bench/
Abstract:Existing full-duplex speech benchmarks cover only subsets of real-time interaction behaviors, often under limited contextual conditions. We introduce DuplexAct-Bench, a bilingual benchmark that systematically covers six complementary behaviors, from interruption and yielding to proactive initiation, active silence, and backchanneling, across Pre-session, In-session, and No-explicit conditions. Across 1,290 English and Chinese streaming trials, we evaluate 12 full-duplex speech systems on both Timing and Content. Results reveal substantial variation across behaviors, conditions, and systems, as well as frequent mismatches between semantic quality and behavioral timing. These findings show that current systems remain far from robustly managing when, whether, and how to participate as real-time interaction unfolds. Project page: this https URL
Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
arXiv:2609.39446 [cs.CL]
(or arXiv:2609.39446v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.39446 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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