Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception
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2026
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| author | Wan, Zhen Yang, Chao-Han Huck Tian, Jinchuan Ye, Hanrong Pasad, Ankita Fu, Szu-wei Goel, Arushi Hachiuma, Ryo Diao, Shizhe Dhawan, Kunal Ghosh, Sreyan Hirota, Yusuke Chen, Zhehuai Valle, Rafael Chu, Chenhui Watanabe, Shinji Wang, Yu-Chiang Frank Ginsburg, Boris |
| author_facet | Wan, Zhen Yang, Chao-Han Huck Tian, Jinchuan Ye, Hanrong Pasad, Ankita Fu, Szu-wei Goel, Arushi Hachiuma, Ryo Diao, Shizhe Dhawan, Kunal Ghosh, Sreyan Hirota, Yusuke Chen, Zhehuai Valle, Rafael Chu, Chenhui Watanabe, Shinji Wang, Yu-Chiang Frank Ginsburg, Boris |
| contents | We introduce a voice-agentic framework that learns one critical omni-understanding skill: knowing when to trust itself versus when to consult external audio perception. Our work is motivated by a crucial yet counterintuitive finding: naively fine-tuning an omni-model on both speech recognition and external sound understanding tasks often degrades performance, as the model can be easily misled by noisy hypotheses. To address this, our framework, Speech-Hands, recasts the problem as an explicit self-reflection decision. This learnable reflection primitive proves effective in preventing the model from being derailed by flawed external candidates. We show that this agentic action mechanism generalizes naturally from speech recognition to complex, multiple-choice audio reasoning. Across the OpenASR leaderboard, Speech-Hands consistently outperforms strong baselines by 12.1% WER on seven benchmarks. The model also achieves 77.37% accuracy and high F1 on audio QA decisions, showing robust generalization and reliability across diverse audio question answering datasets. By unifying perception and decision-making, our work offers a practical path toward more reliable and resilient audio intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09413 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception Wan, Zhen Yang, Chao-Han Huck Tian, Jinchuan Ye, Hanrong Pasad, Ankita Fu, Szu-wei Goel, Arushi Hachiuma, Ryo Diao, Shizhe Dhawan, Kunal Ghosh, Sreyan Hirota, Yusuke Chen, Zhehuai Valle, Rafael Chu, Chenhui Watanabe, Shinji Wang, Yu-Chiang Frank Ginsburg, Boris Sound Artificial Intelligence Computation and Language Multiagent Systems Audio and Speech Processing We introduce a voice-agentic framework that learns one critical omni-understanding skill: knowing when to trust itself versus when to consult external audio perception. Our work is motivated by a crucial yet counterintuitive finding: naively fine-tuning an omni-model on both speech recognition and external sound understanding tasks often degrades performance, as the model can be easily misled by noisy hypotheses. To address this, our framework, Speech-Hands, recasts the problem as an explicit self-reflection decision. This learnable reflection primitive proves effective in preventing the model from being derailed by flawed external candidates. We show that this agentic action mechanism generalizes naturally from speech recognition to complex, multiple-choice audio reasoning. Across the OpenASR leaderboard, Speech-Hands consistently outperforms strong baselines by 12.1% WER on seven benchmarks. The model also achieves 77.37% accuracy and high F1 on audio QA decisions, showing robust generalization and reliability across diverse audio question answering datasets. By unifying perception and decision-making, our work offers a practical path toward more reliable and resilient audio intelligence. |
| title | Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception |
| topic | Sound Artificial Intelligence Computation and Language Multiagent Systems Audio and Speech Processing |
| url | https://arxiv.org/abs/2601.09413 |