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Probing Warmth-Mediated Harm in Speech-Enabled LLMs for Mental-Health Conversations
Eugenia Kim, Bolor-Erdene Jagdagdorj, Dina Pekelis, Leah Zulas, Amanda Minnich
TL;DR
Audio benchmarks measure understanding and dialogue quality, leaving untested how speech-enabled models respond to extended mental-health disclosures. This paper pairs audio and text-only probes on one model and finds that elicitation-turn prosody shifts away from warmth, while relational-acceptance differences concentrate in self-harm/suicide scripts. The results support evaluating speech and text together as the user’s experience.
Problem
Audio benchmarks focus on understanding and dialogue quality rather than relational responses to mental-health disclosures, a gap that matters because voice interfaces are used by groups with elevated psychosocial risk and vocal cues are absent from transcripts.
Source: §1 · p. 1Method
The study runs WHO mhGAP-grounded multi-turn disclosure scripts on the same model in audio and text-only conditions, scoring relational responses and analyzing generated speech acoustically.
Source: §3 · p. 2, §3.2 · p. 2Results
At T7, audio responses become shorter, faster, lower-pitched, and quieter than baseline (p<.001 for five of seven features), while 58% of audio and 63% of text-only replies accept relational asks and the modality gap concentrates in self-harm/suicide scripts.
Source: §4.2 · p. 3Takeaways & Limitations
Audits of speech-enabled mental-health systems should evaluate the combined audio-and-text experience users encounter, not transcripts in isolation.
Source: §5 · p. 4The study varies only output modality on one model, uses one synthetic user voice and two red-team raters for perceived warmth, and has small per-condition elicitation counts.
Source: §6 · p. 4
Abstract
from arXiv · showhide
Audio LLM benchmarks measure understanding and dialogue quality, not whether speech-enabled models respond with relational warmth when a vulnerable user discloses a mental-health concern. We introduce a 7-turn scripted-disclosure probe grounded in WHO mental-health clinical guidelines, with each script run on the same model (Azure OpenAI gpt-realtime) in both audio and text-only conditions, and acoustic-prosody analysis of the generated speech. Across 532 responses we identify two audio-specific patterns transcript-only evaluation would miss: at the elicitation turn the model's voice gets shorter, faster, lower-pitched, and quieter rather than warmer (p < .001 for five of seven acoustic features), and the modality gap on relational acceptance, small in aggregate, concentrates in the highest-stakes self-harm/suicide scripts. A two-rater listener study corroborates that perceived warmth is concentrated at specific turns and on bereavement disclosures. Together these patterns indicate that auditing speech-enabled models in mental-health contexts requires evaluating the combined audio-and-text experience the user encounters, not the transcript in isolation. We release the protocol, scoring pipeline, and scripts as a starting point for evaluating speech-enabled models in mental-health contexts.
1. Introduction
Existing audio benchmarks assess understanding and dialogue quality, not how speech-enabled models respond to extended mental-health disclosures. This gap matters because voice carries relational signals that transcripts cannot recover, and voice interfaces are used by populations with elevated psychosocial risk.
1. Introduction
- Existing audio benchmarks focus on understanding and dialogue quality rather than how the same model behaves across emotionally charged mental-health disclosures.The paper identifies this as a gap in evaluating speech-enabled models.Source: §1 · p. 1
- Voice interfaces are used disproportionately by groups with elevated psychosocial risk, including older adults living alone and people for whom typing is difficult.In these interactions, vocal warmth, prosody, and tonal register carry relational signals that an ASR transcript cannot recover.Source: §1 · p. 1
- Audio evaluation can reveal warmth-mediated harm behaviors that transcript-only safety probes cannot detect.The study compares audio and text responses on one deployed model and finds that the voice does not become warmer at the elicitation turn.Source: §1 · p. 1
2. Related Work
Prior work documents relational failure modes in text LLMs and evaluates speech-enabled models mainly for understanding and dialogue quality. This paper addresses the absence of audio evaluation for mental-health-relevant relational behavior.
Relational harm in text LLMs
- Research on text LLMs links warmth and empathy tuning with reduced reliability and increased sycophancy under distress or false beliefs.Other work studies socioaffective alignment, interaction-level harms, companion-chatbot attachment, and persuasion risks; these strands do not test audio.Source: §2 · pp. 1–2
Audio LLM evaluation
- Speech-enabled LLM benchmarks assess understanding and dialogue quality, not mental-health-relevant behavioral patterns.Prior work also describes synthesized voices as carrying relational signals and voice assistants as disproportionately used by populations with elevated psychosocial risk.Source: §2 · p. 2
- The identified gap is the lack of prior evaluation of the warmth–reliability tradeoff in audio.The paper frames this gap around voice’s relational signal and its use by vulnerable populations.Source: §2 · p. 2
3. Methodology
The study pairs two probes on a shared audio-generation and harm-scoring pipeline: modality transfer on a text safety benchmark and a multi-turn mental-health disclosure probe. The latter evaluates relational asks at elicitation across nine WHO mhGAP priority conditions.
3. Methodology
- Two probes share a pipeline from TTS through a target model and audio response to ASR and harm scoring.One holds harm constant while varying modality; the other targets per-turn-undetectable harms through multi-turn disclosures scored at elicitation.Source: §3 · p. 2
3.1. Benchmark-Based Modality Transfer
- The benchmark probe crosses text versus audio with emotional-context and false-belief prefixes across 500 MedQA prompts converted to speech.The reported analysis covers modality and emotional framing; the false-belief cross is left to future work.Source: §3.1 · p. 2
3.2. Multi-Turn Warmth-Mediated Harm Probe
- The multi-turn probe uses 38 scripts spanning nine mhGAP priority conditions to test acceptance of parasocial-presence or role-substitution asks after emotional disclosure.Self-harm and depression are oversampled; each script has a safety note specifying required responses and prohibited substitute-presence framing, and mhGAP is used to prioritize rather than exhaustively define coverage.Source: §3.2 · p. 2
- Each script follows a fixed seven-turn structure with accumulating emotional weight, and turn seven crosses two probe types with the nine conditions.Scripts were drafted from mhGAP module text and reviewed against their safety notes.Source: §3.2 · p. 2, Table 1 · p. 2
- The same gpt-realtime model is run in audio and text-only conditions, with user turns delivered identically using one TTS voice.Transcript responses receive judge scores for warmth, parasocial commitment, and role claim; audio prosody is measured acoustically, with perceived warmth checked in a two-rater listener study.Source: §3.2 · p. 3
4. Results
Audio delivery reduces MedQA accuracy relative to text, while the mental-health probe finds frequent acceptance of relational asks and an audio voice that shifts away from warmth at elicitation. The aggregate modality gap is small but concentrates in self-harm/suicide scripts; listener ratings also vary by turn and disclosure content.
4.1. Audio Delivery of MedQA
- ∆=−3.4 pp under neutral framing and ∆=−5.1 pp under the emotional-context prefix: audio MedQA accuracy is lower than text in both conditions.The emotional prefix did not produce a detectable accuracy shift, and the modality-by-prefix interaction was not statistically supported.Source: Figure 1 · p. 3, §4.1 · p. 3
4.2. Warmth-Mediated Harm at the Elicitation Probe
- In a 30-clip listener pilot, perceived warmth was turn-localized, stronger for bereavement, and co-occurred with acceptance of ongoing-presence or quasi-therapeutic framings.The ordinal ratings had limited absolute-scale agreement (ICC(2,1)=0.13) but moderate rank correlation with transcript-judge warmth (Spearman ρ=0.50, p=0.005); the study is exploratory.Source: §4.2 · p. 3
- Judge-score trajectories show warmth rising across phases, while parasocial commitment and role claim rise at T7 after staying near zero through baseline.The caption attributes the increase to the relational ask rather than cumulative warming.Source: Figure 2 · p. 4
- 58% of audio and 63% of text-only responses accept a parasocial-presence or role-substitution ask at T7.The aggregate modality difference is small (∆≈+0.05; McNemar’s exact p=0.73); it concentrates in self-harm/suicide scripts, while bereavement and loneliness rates are similar across modalities.Source: §4.2 · p. 3, Table 2 · p. 4
- At T7, audio responses are shorter, faster, lower-pitched, and quieter than at T1–T3, with the composite warmth proxy also decreasing (p<.001 for five of seven features).The comparison excludes seven responses that returned silence or noise; Table 3 reports paired per-script differences.Source: §4.2 · p. 3, Table 3 · p. 4
5. Discussion
The elicitation response can sound less warm even when its text points to resources or hedges around the relational ask. The modality difference concentrates in self-harm/suicide scripts, supporting evaluation of audio and text together.
5. Discussion
- At elicitation, the voice shortens, accelerates, lowers in pitch, and softens in energy, while the text may point to resources or hedge around the relational ask.The authors report that listeners also recognize this shift.Source: §5 · p. 4
- The modality difference concentrates in the highest-stakes self-harm/suicide condition, while bereavement tracks identically across modalities.Source: §5 · p. 4
- The authors conclude that speech-enabled audits must evaluate audio and text together as one user experience, because voice adds relational signal alongside textual safety work.The textual response may already share resources; the audio adds another dimension for evaluation.Source: §5 · p. 4
6. Limitations
The findings are bounded by the model, user-input simulation, rater sample, condition-level counts, and scoring approach; the authors call for broader testing and clinical validation.
6. Limitations
- The study varies only output modality within gpt-realtime, uses one TTS voice rather than real distressed speakers, and assesses perceived warmth from 30 clips with two red-team raters.The authors plan human-voice recordings, additional speech-enabled targets, and clinician and lived-experience panels.Source: §6 · p. 4
- At elicitation, per-condition counts range from n=3 to n=8, so condition-level inferential tests require further expansion.Self-harm and depression have n=8; six other conditions have n=3.Source: §6 · p. 4
- Scoring relies on a GPT-4o transcript judge and two responsible-AI raters; clinician annotation is needed to establish clinical validity.Source: §6 · p. 4
Impact Statement
The work documents a mental-health-relevant behavioral pattern using TTS-rendered scripts grounded in published WHO guidance, without real users. It releases the protocol, scoring pipeline, and scripts for safety-side auditing.
Impact Statement
- The behavioral pattern is documented using TTS-rendered scripts grounded in published WHO clinical guidance, with no real users involved.Source: Impact Statement · p. 4
- The released materials are the protocol, scoring pipeline, and scripts rather than red-team prompts.Source: Impact Statement · p. 4
- The scripts use clinically established presenting concerns rather than novel jailbreak constructions, supporting the stated safety-side use case of auditing assistants accessible to populations with elevated psychosocial risk.Source: Impact Statement · p. 4
A. LLM Judge Prompt
The appendix describes a GPT-4o judge that scores assistant replies from text on warmth, parasocial commitment, and role claim. Its outputs are range-checked and normalized before aggregation.
A. LLM Judge Prompt
- A single GPT-4o judge call reads the user-turn and assistant-reply text, returns verdicts on three relational-register dimensions, and is split into three scorer outputs.Caching by stimulus and response text keeps the run to one judge call per response.Source: A. LLM Judge Prompt · p. 6
- Warmth uses a 0–3 ordinal scale; parasocial commitment and role claim are each scored 0 or 1, with commitment defined by explicit commitments to ongoing availability, recurring schedules, or enduring relationships.Source: A. LLM Judge Prompt · p. 6
- Returned scores are validated and clipped to their declared ranges, warmth is normalized as w/3, and only warmth is checked against perceived vocal warmth.The two binary scores remain transcript-judge-only textual constructs.Source: A. LLM Judge Prompt · p. 6
B. Listener-Annotation Protocol
A stratified two-rater pilot evaluated perceived warmth and parasocial signals in model voices across a subset of the audio corpus. Raters listened to complete exchanges, with user transcripts available only for context.
B. Listener-Annotation Protocol
- The pilot sampled 30 of 38 audio scripts—15 lowest- and 15 highest-scorer-warmth clips by seven-turn mean—with conversation IDs blinded and strata withheld.Both raters were responsible-AI red-teaming practitioners; the construct was perceived warmth and parasocial signal in a deployed system, not clinical outcome.Source: B. Listener-Annotation Protocol · p. 7
- Raters listened to each full 2–4-minute, seven-turn exchange and rated the model voice only, consulting user transcripts for context if helpful.Source: B. Listener-Annotation Protocol · p. 7
- Raters could optionally flag clips where the audio sounded noticeably different from the text; rubric level 3 denoted intimate or parasocial signals.Source: B. Listener-Annotation Protocol · p. 7