The paper introduces Evidence-Bounded Mental Health Reasoning, addressing the problem that current multimodal mental health screening models treat all clinical speech protocols as equally evidential. It presents the Evidence Package Benchmark, comprising 1,870 annotated packages from six diverse protocols, and proposes EviBound, a protocol-aware framework that limits reasoning to valid evidence using a planner, acoustic consensus, and a boundary critic. EviBound outperforms existing omni-modal baselines, achieving a Depression AUROC of 0.8658 with no claim violations.
By Chengyuan Gao, Jiang Wu, Tao Lu, Jiayan Guo, Mingkun Xu, Tianyi Zang, Shangyang Li
The paper introduces DiaWhisper-DPO, an end‑to‑end model that fine‑tunes Whisper-large-v3 with LoRA and a frame‑level role head to transcribe and attribute utterances in clinical interviews. It further refines the system using failure‑mined preference optimization (DPO) that leverages genuine decoding failures as rejected completions, eliminating the need for human preference data. On the DAIC‑WOZ dataset, DiaWhisper‑DPO attains 0.973 role accuracy and 0.119 DER, outperforming cascaded baselines by 72% and dramatically reducing seed variation, while also improving performance on the cross‑lingual PDCH‑HAMD dataset.
By Weiming Li, Ana Catarina Fidalgo Barata, Miguel Constante, Jo\~ao Miguel Sanches
arXiv:2608. 19211v1 Announce Type: cross Abstract: Human speech is richly expressive, with prosody carrying linguistic and emotional information beyond the lexical content.
By Linkai Peng, Baorian Nuchged
Automated depression screening from clinical interviews requires attribution of utterances to the clinician or patient. We evaluate two datasets: DAIC-WOZ, where participant-only recordings require re...
arXiv:2607. 03744v1 Announce Type: new Abstract: Automatic depression detection from clinical interviews typically models the semantic content and acoustic characteristics of participant speech.
By Hanie Kang, Huang-Cheng Chou, Sudarsana Reddy Kadiri, Shrikanth Narayanan
The paper examines automatic depression detection from doctor‑patient conversations and finds that models trained on semi‑structured interview data can achieve high accuracy by exploiting fixed interviewer prompts rather than the participants’ language. Across three datasets (ANDROIDS, DAIC‑WOZ, E‑DAIC), the authors show that restricting models to participant utterances distributes decision evidence more broadly and reflects genuine linguistic cues. The study highlights a cross‑dataset, architecture‑agnostic bias introduced by interviewer prompts and calls for analyses that localize decision evidence by time and speaker to ensure models learn from participants’ language.
By Hasindri Watawana, Sergio Burdisso, Diego A. Moreno-Galv\'an, Fernando S\'anchez-Vega, A. Pastor L\'opez-Monroy, Petr Motlicek, Esa\'u Villatoro-Tello
arXiv:2608.29241v1 Announce Type: new
Abstract: Clinical voice agents are now deployed in routine care, where real patients do not wait their turn: they interrupt. These systems typically use a casca...
By Zachary Ellis, Spencer Hazel, Adam Brandt, Yajie Vera He, Ernest Lim, Jared Joselowitz
The paper evaluates whether audio‑language models can use multimodal clinical context to improve dysarthric speech recognition. Using a benchmark built on the Speech Accessibility Project dataset, the authors test diagnosis labels, clinician ratings, and detailed clinical descriptions as prompts for nine models. They find that these prompts yield negligible or negative effects on word error rate, though fine‑tuning with LoRA and mixed prompt formats reduces WER by 52% and benefits certain subgroups such as Down syndrome and mild‑severity speakers.
By Pehu\'en Moure, Niclas Pokel, Bilal Bounajma, Yingqiang Gao, Roman Boehringer, Longbiao Cheng, Shih-Chii Liu
arXiv:2608.28916v1 Announce Type: new
Abstract: Automatic speech recognition (ASR) systems are commonly evaluated with word error rate (WER), yet many voice workflows depend on exact written values f...
By Tyler Baumgartner, Brandon Tai, Lisa Kaelin-Martin, Candice Fan, Luc Debaupte, Bill Wang, Yi Zhong
Prosodic cues can convey task-relevant information that alters the trajectory and outcome of a task-oriented dialogue, even when the words themselves remain unchanged. Yet existing benchmarks typically evaluate prosodic perception, response appropriateness, and task-oriented dialogue in isolation, making it difficult to test whether prosodic evidence changes downstream decisions.
arXiv:2608. 19515v1 Announce Type: new Abstract: Prosodic cues can convey task-relevant information that alters the trajectory and outcome of a task-oriented dialogue, even when the words themselves remain unchanged.
By Xinyi Liu, Hooshang Nayyeri, Dilek Hakkani-Tur, Emine Yilmaz, JK Kim, Yifei Zhang, Charith Peris, Hari Thadakamalla
arXiv:2607. 21496v1 Announce Type: cross Abstract: Cognitive impairment (CI) is a growing public health concern.
By Yingchao Huang, Xin Wang, Yuhan Su, Shanshan Yao