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Lost in Phonation: Voice Quality Variation as an Evaluation Dimension for Speech Foundation Models

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Electrical Engineering and Systems Science > Audio and Speech Processing

arXiv:2510.25577 (eess)
[Submitted on 29 Oct 2025 (v1), last revised 14 Aug 2026 (this version, v2)]

Title:Lost in Phonation: Voice Quality Variation as an Evaluation Dimension for Speech Foundation Models

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Abstract:Recent advances in Speech Foundation Models (SFMs) enable direct processing of raw audio, allowing models to respond to subtle paralinguistic variation. However, how these models interpret non-lexical cues remains largely unstudied. We introduce VQ-Bench, a controlled evaluation suite featuring a parallel dataset of synthesized modal, breathy, creaky, and end-creak phonation types. We evaluate SFM sensitivity through open-ended generation across four ecologically valid domains, alongside speech emotion recognition. Our results reveal performance gaps: while a leading commercial API failed basic biometric sanity checks, other models exhibited systematic shifts in agency, empathy, and leadership based on phonation. Our findings also highlight gender asymmetries in salary and leadership endorsements, demonstrating that SFMs may mirror or amplify human social biases. This work establishes a reproducible framework for ensuring responsible paralinguistic interpretation in speech-based AI.
Comments: 5 pages, 2 figures, 3 tables, accepted at Interspeech 2026
Subjects: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2510.25577 [eess.AS]
  (or arXiv:2510.25577v2 [eess.AS] for this version)
  https://doi.org/10.48550/arXiv.2510.25577
arXiv-issued DOI via DataCite

Submission history

From: Harm Lameris [view email]
[v1] Wed, 29 Oct 2025 14:44:44 UTC (77 KB)
[v2] Fri, 14 Aug 2026 08:55:35 UTC (79 KB)
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