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        <title>Speculative End-Turn Detector for Efficient Speech Chatbot Assistant</title>
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    <abstract>Spoken dialogue systems powered by large language models have demonstrated remarkable abilities in understanding human speech and generating appropriate spoken responses.However, these systems struggle with end-turn detection (ETD)—the ability to distinguish between user turn completion and hesitation. This limitation often leads to premature or delayed responses, disrupting the flow of spoken conversations.In this paper, we introduce the OpenETD Dataset, the first public dataset for end-turn detection. The OpenETD dataset consists of both synthetic speech data generated with text-to-speech models and real-world speech data collected from web sources. We also propose SpeculativeETD, a novel collaborative inference framework that balances efficiency and accuracy to improve real-time ETD in resource-constrained environments. Our approach jointly employs a lightweight GRU-based model, which rapidly detects the non-speaking units in real-time on local devices, and a high-performance Wav2vec-based model running on the server to make a more challenging classification of distinguishing turn ends from mere pauses. Experiments demonstrate that the proposed SpeculativeETD significantly improves ETD accuracy while keeping the required computations low.</abstract>
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%0 Conference Proceedings
%T Speculative End-Turn Detector for Efficient Speech Chatbot Assistant
%A Ok, Hyunjong
%A Yoo, Suho
%A Lee, Jaeho
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F ok-etal-2026-speculative
%X Spoken dialogue systems powered by large language models have demonstrated remarkable abilities in understanding human speech and generating appropriate spoken responses.However, these systems struggle with end-turn detection (ETD)—the ability to distinguish between user turn completion and hesitation. This limitation often leads to premature or delayed responses, disrupting the flow of spoken conversations.In this paper, we introduce the OpenETD Dataset, the first public dataset for end-turn detection. The OpenETD dataset consists of both synthetic speech data generated with text-to-speech models and real-world speech data collected from web sources. We also propose SpeculativeETD, a novel collaborative inference framework that balances efficiency and accuracy to improve real-time ETD in resource-constrained environments. Our approach jointly employs a lightweight GRU-based model, which rapidly detects the non-speaking units in real-time on local devices, and a high-performance Wav2vec-based model running on the server to make a more challenging classification of distinguishing turn ends from mere pauses. Experiments demonstrate that the proposed SpeculativeETD significantly improves ETD accuracy while keeping the required computations low.
%U https://aclanthology.org/2026.acl-long.2094/
%P 45184-45197

Markdown (Informal)

[Speculative End-Turn Detector for Efficient Speech Chatbot Assistant](https://aclanthology.org/2026.acl-long.2094/) (Ok et al., ACL 2026)

• Speculative End-Turn Detector for Efficient Speech Chatbot Assistant (Ok et al., ACL 2026)

ACL

• Hyunjong Ok, Suho Yoo, and Jaeho Lee. 2026. Speculative End-Turn Detector for Efficient Speech Chatbot Assistant. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 45184–45197, San Diego, California, United States. Association for Computational Linguistics.