Word-Aligned Prosody Reflects Speaker Differences More Than Depression or PTSD Severity
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Abstract (arXiv)
Word-aligned prosody is increasingly used to detect depression and posttraumatic stress disorder (PTSD), yet the stability and clinical relevance of these measurements remain unclear. We examine stability at two levels. At the word level, we ask whether a speaker produces the same word in a similar speaker-specific way across different utterance contexts. At the speaker level, we ask whether averaging prosody across many words produces consistent speaker scores across separate recordings. Drawing on principles of psychological measurement, we compare these representations in four corpora spanning acted, spontaneous, and clinical speech. Word-specific deviations show little cross-context recurrence (0.01-0.16), whereas speaker summaries are highly consistent, reaching 0.997 in ESD, 0.73-0.90 in MSP-Podcast, and 0.84-0.996 in DAIC-WOZ and PTSD-STOP. Despite this consistency, associations with depression or PTSD severity do not exceed 0.15; equivalence tests exclude effects of 0.30 in all analyses and 0.20 in DAIC-WOZ, while within-person associations with daily PTSD symptoms are near zero (|ρ|≤0.04). Thus, aggregation yields stable speaker-level prosodic measures, but these measures do not detectably track depression or PTSD severity in the studied corpora. Code is available at https://github.com/jen900704/word-aligned-prosody.
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References · 8
- 2023WhisperX: Time-Accurate Speech Transcription of Long-Form Audiocited by 326
- 2020Seen and Unseen emotional style transfer for voice conversion with a new emotional speech datasetcited by 121
- 2025The MSP-Podcast Corpuscited by 16
- 2024DAIC-WOZ: On the Validity of Using the Therapist's prompts in Automatic Depression Detection from Clinical Interviewscited by 15
- 2018The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictionscited by 10
- 2025CogniAlign: Word-Level Multimodal Speech Alignment with Gated Cross-Attention for Alzheimer's Detectioncited by 7
- 2026From Word Sequences to Behavioral Sequences: Adapting Modeling and Evaluation Paradigms for Longitudinal NLPcited by 4
- 2026Who is Speaking or Who is Depressed? A Controlled Study of Speaker Leakage in Speech-Based Depression Detectioncited by 2
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