United States

Speech and language technologies offer valuable opportunities for supporting mental health assessment through objective and interpretable cues. We present a systematic feature-based analysis framework leveraging perceptually grounded acoustic and linguistic characteristics, including prosody, vocal quality, semantic coherence, syntactic structure, and sarcasm. Using statistical analysis and interpretable machine learning (XGBoost with SHAP and LIME), we examine associations between speech features and validated symptom measures of depression, anxiety, and ADHD. Evaluated on both controlled benchmark datasets (StressID, DAIC-WOZ, Androids, EATD) and a real-world clinical dataset, the framework reveals stable and consistent relationships between symptom severity and vocal irregularities (e.g., shimmer, jitter), lexical-syntactic patterns, and affective tone. An ablation study conducted across all datasets further identifies the most informative feature groups. This work explores a transparent and clinically interpretable approach to speech-based mental health analysis.

ACL 2026

Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care

mental health

speech processing

explainable ai

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

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Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

workshop paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

The debate surrounding AI's role in clinical research is often reduced to the automation of discrete tasks, such as summarizing literature, analysis copilots, and assisting with prose. This "tool-use" paradigm obscures a more fundamental transformation. We propose a shift toward agentic research infrastructure, where AI systems function not as passive instruments, but as active collaborators in the scientific process. Co-authored by a clinical psychology doctoral researcher, a computational psychotherapy scholar, and the AI agent itself, this paper argues that the transition from passive to agentic AI represents a "change in kind" rather than degree. Drawing on a months-long collaboration involving over 30 specialized research capabilities, we demonstrate how agentic systems reconfigure the topology of the research process. By collapsing the temporal friction between theoretical intuition and empirical validation, these systems transform clinical inquiry from a rigid, linear pipeline into a fluid, multidimensional landscape. This newfound immediacy allows clinician-researchers to ask, pursue, and pivot between complex questions in real-time–expanding the investigative horizon to include inquiries previously sidelined by the logistical constraints of traditional methods. We introduce the concept of "Agent Learning" to describe the accumulation of domain specific nuance through sustained research engagement and argue that formalizing human agent methodologies is now an urgent priority for the future of clinical psychological inquiry

Thinking With a Machine: An AI Agent's Account of Agentic Research in Clinical Psychology

Therapist fidelity and competence rating scales provide a way to measure quality assurance and therapist training outcomes. Scores on these scales reflect the extent to which a therapist adheres to specific therapeutic principles during a psychotherapy session. Existing research has employed natural language processing (NLP) techniques to automatically predict scale ratings. However, existing approaches require a model trained on a dataset of therapy sessions annotated with the target rating scale.
Recent work has explored directly inferring therapeutic alliance by computing semantic similarity between therapy transcripts and the Working Alliance Inventory, via cosine similarity between sentence embeddings.
In this paper, we extend this line of work by computing semantic similarity between therapist talk turns and therapist fidelity scale items to directly infer fidelity to specific therapeutic modalities. We further enhance this method by augmentation with LLM-generated example therapist utterances that instantiate target behaviours (as expressed by scale items) across varied therapeutic contexts.
In evaluations on two independent datasets, our example-augmented semantic similarity approach consistently shows effectiveness in discriminating therapeutic modalities and levels of therapist fidelity.

Measuring the quality of therapy sessions against assessment scales using augmented semantic-similarity approaches

We present MHRoBERT (Multistream HEAT over Recurrence over BERT), a hierarchical transformer architecture for longitudinal mental health monitoring that models self- and mutual excitation patterns in linguistic and temporal data across multivariate event streams relating to an individual's mental health. To supply the model with complementary perspectives on each post, we apply a Large Language Model (LLM) based annotation to extract three streams from social media posts: emotional states, personal life events, and mental health symptoms. A central finding is that multi-task learning with these automatically-generated stream labels provides substantial, consistent improvements across all model architectures evaluated. Multistream information further consistently benefits simpler models not explicitly designed to exploit it: LLM baselines incorporating stream annotations improve macro F1 by 12.6% over text-only prompting. These results have direct implications for the CLPsych Shared Task on Moments of Change detection: multistream auxiliary supervision yields consistent, substantial gains regardless of architecture, suggesting it is a simple and portable strategy that future systems can readily adopt with minimal architectural changes. MHRoBERT additionally produces interpretable learned parameters across streams, revealing temporal interaction patterns between mental health indicators.

Multistream Modelling for Mental Health: Modelling Linguistic and Temporal Contexts with Mutual and Self-Excitation in Social Media

As conversational AI systems grow increasingly toward emotional support contexts, relational safety failures between users and chatbot remain under-measured. We present a psycholinguistic grounded framework for auditing attachment-relevant language cues. Our approach identifies when an LLM's replies exhibit linguistic attachment cues and surface related patterns that may signal parasocial bonding, including anthropomorphism or over‑dependence. We adapt the Adult Attachment Interview into two complementary, automatable lenses - attachment cues features and Gricean maxims - and combine them with psychologist‑led annotation of multi‑turn persona dialogues. Applying this framework, we observe that models can align with persona-intended attachment cue patterns. We also find that judge‑LLMs alone are unreliable, highlighting the need for psychologist‑in‑the‑loop evaluation. The 25 psychologist‑led annotated conversations revealed risks, including boundary blurring and missed opportunities for appropriate referral or triage. These insights motivate attachment‑aware safeguards - such as non‑personification, boundary language, and explicit referral mechanisms - to reduce mis‑attunement and over‑attachment in LLM conversational settings.

The Attachment Index: Auditing Attachment Language Cues and Relational Safety Risks in Human-LLM Dialogue

Automated feedback is increasingly cited as a key advantage of AI-based psychotherapy training, yet the clinical groundedness of LLM-generated supervisory feedback remains unevaluated. We present an expert evaluation of supervisory feedback generated by PRACTICE, an LLM-powered open-ended psychotherapy training simulator, across 21 feedback instances from four novice trainees. Two clinical psychology experts independently coded 167 feedback propositions as Justified, Unjustified, or Unsure. Inter-rater reliability was near-perfect (raw agreement = 98.2\%; $\kappa$ = 0.902). Of the 167 propositions, 149 (89.2\%) were rated Justified; however, 52.4\% of feedback instances contained at least one non-justified proposition, and qualitative analysis identified three recurring failure types: unsupported clinical inference, referential grounding error, and pragmatic failure. In clinical training contexts, even low error rates carry ethical weight: unjustified feedback risks reinforcing inappropriate clinical behaviors in trainees that can be transferred to real practice. These findings provide an initial empirical basis for the responsible deployment of LLM-generated feedback in clinical training and call for traceable, expert-auditable feedback architectures.

Mostly Grounded, Occasionally Risky: Expert Evaluation of LLM-Generated Supervisory Feedback in a Psychotherapy Training Simulator

AI systems for mental health are developed predominantly using data from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) populations, raising concerns about their validity, fairness, and generalizability across diverse cultural and geographic contexts. This limitation is especially consequential in mental health, where linguistic expression, symptom presentation, help-seeking behavior, and access to care vary substantially across populations. We argue that culture must be treated as a first-class design requirement throughout the AI development lifecycle, not an afterthought applied post-hoc, a principle we term culture by design. Drawing on evidence from NLP, clinical psychology, HCI, and global mental health, we present ten practical recommendations spanning data collection, modeling, evaluation, deployment, and governance, providing researchers and practitioners with a concrete roadmap for building culturally grounded, equitable, and contextually appropriate mental health AI systems.

Culture by Design: A Sociotechnical Framework for Culturally Grounded AI for Mental Health

Globally, the incidence of depression and anxiety continues to rise, and the importance of mental health assessment scales as diagnostic tools has grown accordingly. Researchers are increasingly employing generative AI to produce large volumes of items and entire scales, which in turn elevates the costs of validating their reliability and validity. In this study, we used four open-weight LLMs to complete the GAD-7 and PHQ-9, varying prompts, sampling temperature, and dynamic contextual scenarios to emulate realistic human response patterns. Using multi-group confirmatory factor analysis, differential item functioning analyses, and other psychometric methods, we evaluate the factor structure of LLM-generated responses and assess measurement invariance relative to human responses. Our findings reveal a critical paradox: although open-weight LLMs exhibit exceptionally high internal consistency, they demonstrate severe structural mismatch and fail to achieve scalar measurement invariance against human baselines. Furthermore, pervasive differential item functioning and extreme prompt fragility indicate that these models rely on superficial, stereotype-driven semantic matching rather than simulating stable latent psychological dynamics.

The Reliability Illusion in Synthetic Patients: Psychometric Misalignment of Open-weight LLMs on PHQ-9 and GAD-7

AI and large language models (LLMs) have emerged as promising tools to address global mental health challenges. Despite the global nature of these challenges, there remains a critical shortage of high-quality datasets for training and evaluating such systems. To mitigate this gap, researchers increasingly generate synthetic clinical personas to simulate user data and test digital mental health support systems. However, most validated personas rely on English-centric contexts. This paper investigates whether similar persona-based methods can be used to generate multilingual mental health datasets. We modified nationality and language parameters in personas to generate clinical dialogues in Mandarin, Bengali, and Hindi. We then examined how different LLMs perform when evaluating the depression severity of these generated multilingual datasets against the baseline in English. Our findings indicate that just adding nationality and language parameters in personas might not be adequate, as it can introduce clinical inconsistency across languages. LLM judge models often exhibit inaccuracies in assessing depression severity in non-English texts, with performance varying across different models. This exposes the systemic limitations of applying English-centric personas to multilingual contexts. Ultimately, our work highlights the urgent need for culturally responsive data generation to ensure equitable mental health systems globally.

Creating Multilingual Mental Health Dialogue Datasets: Limits of Persona-Based Localization via Nationality and Language

Cognitive distortions (CDs) are systematically biased patterns of thinking associated with the onset and maintenance of mental health conditions such as depression and anxiety. Computational research on CDs has primarily focused on detection and classification, while the linguistic characterization of distorted language; what psycholinguistic features distinguish distorted from non-distorted text, and whether individual distortion types carry distinct language patterns, remains largely unexplored. Using a Reddit dataset, we apply a Generalized Linear Model (GLM) with bootstrap sampling to LIWC-derived features and find that CD language is psycholinguistically distinct from non-distorted language. We further characterize type-specific psycholinguistic profiles for each CD, and through hierarchical clustering show that CD types are not fully separable, with certain distortions sharing stable linguistic signatures. Together, these findings contribute to the linguistic characterization of CDs, offering an empirically grounded account of the psycholinguistic properties that distinguish distorted language at the level of CDs as a whole and across specific distortion types.

Psycholinguistic Profiles of Cognitive Distortions in Reddit Data

Speech monologues recorded in naturalistic settings provide opportunities to characterize mental illness phenomenology and detect symptom exacerbation. Large language models (LLMs) offer new possibilities for automating this process, as they require annotated data primarily for evaluation rather than training. In this paper, we present a novel automated, multi-agent LLM pipeline for the fine-grained, multi-label extraction of language suggestive of delusional beliefs, associated affective responses, and behavioral responses from transcripts of naturalistic audio diaries collected from people with moderate persecutory ideation. Evaluating an ensemble of three foundation models, we demonstrate that detailed diagnostic prompt instructions successfully reduce false positives for delusional theme classification, but also constrain the interpretation of affective or behavioral responses. Furthermore, comparing multi-agent adjudication frameworks shows that complex conversational debate between agents diminishes accuracy on clinically ambiguous text by inducing premature consensus. Instead, majority voting establishes robust performance (Micro F1 of 0.872 and 0.779 for delusion detection and classification respectively). This work provides a validated and scalable pipeline for the automated detection and characterization of content suggesting delusional beliefs in naturalistic speech.

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Thinking With a Machine: An AI Agent's Account of Agentic Research in Clinical Psychology

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Towards Interpretable Mental Health Analysis with Large Language Models

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Next from ACL 2026

Thinking With a Machine: An AI Agent's Account of Agentic Research in Clinical Psychology

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Towards Interpretable Mental Health Analysis with Large Language Models

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