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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/2604.10161 |
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| _version_ | 1866918439592919040 |
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| author | Yang, Xingjian Yang, Yudong Guo, Zhixing Zhou, Yongjie Yan, Nan Wang, Lan |
| author_facet | Yang, Xingjian Yang, Yudong Guo, Zhixing Zhou, Yongjie Yan, Nan Wang, Lan |
| contents | The psychological profile that structurally documents the case of a depression patient is essential for psychotherapy. Large language models can be applied to summarize the profiles from counseling speech, however, it may suffer from long-context forgetting and produce unverifiable hallucinations, due to overlong length of speech, multi-party interactions and unstructured chatting. Hereby, we propose a StreamProfile, a streaming framework that processes counseling speech incrementally, extracts evidences grounded from ASR transcriptions by storing it in a Hierarchical Evidence Memory, and then performs a Chain-of-Thought pipeline according to PM+ psychological intervention for clinical reasoning. The final profile is synthesized strictly from those evidences, making every claim traceable. Experiments on real-world teenager counseling speech have shown that the proposed StreamProfile system can accurately generate the profiles and prevent hallucination. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10161 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Speech to Profile: A Protocol-Driven LLM Agent for Psychological Profile Generation Yang, Xingjian Yang, Yudong Guo, Zhixing Zhou, Yongjie Yan, Nan Wang, Lan Sound The psychological profile that structurally documents the case of a depression patient is essential for psychotherapy. Large language models can be applied to summarize the profiles from counseling speech, however, it may suffer from long-context forgetting and produce unverifiable hallucinations, due to overlong length of speech, multi-party interactions and unstructured chatting. Hereby, we propose a StreamProfile, a streaming framework that processes counseling speech incrementally, extracts evidences grounded from ASR transcriptions by storing it in a Hierarchical Evidence Memory, and then performs a Chain-of-Thought pipeline according to PM+ psychological intervention for clinical reasoning. The final profile is synthesized strictly from those evidences, making every claim traceable. Experiments on real-world teenager counseling speech have shown that the proposed StreamProfile system can accurately generate the profiles and prevent hallucination. |
| title | From Speech to Profile: A Protocol-Driven LLM Agent for Psychological Profile Generation |
| topic | Sound |
| url | https://arxiv.org/abs/2604.10161 |