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This research clarifies the conditions under which lightweight LoRA adaptation effectively improves Whisper-style ASR models, revealing that gains are dataset-dependent and often achievable with pooled adapters rather than group-specific ones. The findings caution against interpreting all performance improvements over zero-shot baselines as evidence of successful personalization without rigorous testing against matched pooled adaptation controls.
Understanding Personalization Gains in Whisper
arXiv:2610.17019v1 Announce Type: new Abstract: Whisper-style ASR models provide strong zero-shot transcription, but their residual errors are not evenly distributed across datasets, recording conditions, and metadata-based groups. We study when lightweight low-rank adaptation (LoRA) improves Whisper and whether observed gains require group-specific adapters. Across Common Voice, LibriSpeech, and VoxPopuli, we compare zero-shot Whisper, pooled LoRA, and group-specific LoRA under matched within-group splits. We find that adaptation is not uniformly beneficial: gains are concentrated in difficult Common Voice groups, are consistent on VoxPopuli, and largely disappear on low-error LibriSpeech. Crucially, pooled LoRA often matches, and sometimes clearly outperforms, group-specific LoRA. Gains over zero-shot should therefore be tested against matched pooled adaptation before being interpreted as evidence of personalization.
Abstract:Whisper-style ASR models provide strong zero-shot transcription, but their residual errors are not evenly distributed across datasets, recording conditions, and metadata-based groups. We study when lightweight low-rank adaptation (LoRA) improves Whisper and whether observed gains require group-specific adapters. Across Common Voice, LibriSpeech, and VoxPopuli, we compare zero-shot Whisper, pooled LoRA, and group-specific LoRA under matched within-group splits. We find that adaptation is not uniformly beneficial: gains are concentrated in difficult Common Voice groups, are consistent on VoxPopuli, and largely disappear on low-error LibriSpeech. Crucially, pooled LoRA often matches, and sometimes clearly outperforms, group-specific LoRA. Gains over zero-shot should therefore be tested against matched pooled adaptation before being interpreted as evidence of personalization.
14 pages. Presented at SPECOM 2026
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2610.17019 [cs.AI]
(or arXiv:2610.17019v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.17019 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
Lecture Notes in Computer Science, vol. 16934, pp. 3-16, Springer (2027)
https://doi.org/10.1007/978-3-032-37867-5_1 Focus to learn more DOI(s) linking to related resources
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