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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.14449 |
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| _version_ | 1866917344874332160 |
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| author | He, Zihong Liang, Hai-Ning Liang, Chen |
| author_facet | He, Zihong Liang, Hai-Ning Liang, Chen |
| contents | Response timing judgment is a critical component of interactive speech agents. Although there exists substantial prior work on turn modeling and voice wake-up, there is a lack of research on response timing judgments continuously aligned with user intent. To address this, we propose the Tap-to-Adapt framework, which enables users to naturally activate or interrupt the agent via tap interactions to construct online learning labels for response timing models. Under this framework, Dilated TCN and a sequential replay strategy play significant roles, as demonstrated through data-driven experiments and user studies. Additionally, we develop an evaluation and continuous data mining system tailored for the Tap-to-Adapt framework, through which we have collected approximately 20,000 samples from the user studies involving 20 participants. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14449 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Tap-to-Adapt: Learning User-Aligned Response Timing for Speech Agents He, Zihong Liang, Hai-Ning Liang, Chen Human-Computer Interaction Response timing judgment is a critical component of interactive speech agents. Although there exists substantial prior work on turn modeling and voice wake-up, there is a lack of research on response timing judgments continuously aligned with user intent. To address this, we propose the Tap-to-Adapt framework, which enables users to naturally activate or interrupt the agent via tap interactions to construct online learning labels for response timing models. Under this framework, Dilated TCN and a sequential replay strategy play significant roles, as demonstrated through data-driven experiments and user studies. Additionally, we develop an evaluation and continuous data mining system tailored for the Tap-to-Adapt framework, through which we have collected approximately 20,000 samples from the user studies involving 20 participants. |
| title | Tap-to-Adapt: Learning User-Aligned Response Timing for Speech Agents |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.14449 |