Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning
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Electrical Engineering and Systems Science > Audio and Speech Processing
Title:Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning
Abstract:Recent advances in model design and self-supervised audio representations have improved speech and audio understanding, yet infant-centered naturalistic recordings remain challenging due to limited labeled data, low signal-to-noise ratio, and cross-family domain shifts. We present a family-conditioned, multi-tier audio tagger that combines a LoRA-finetuned Whisper encoder with a lightweight, target-speaker-aware Transformer for long-context inference and framewise prediction across tiers. To improve temporal coherence, we incorporate a simple sequence-level smoothing loss, and to enhance robustness across households, we introduce a factorized speaker-token design with a shared tier token and a learned family-specific offset, reducing family bias and promoting generalizable representations. Together, these choices enable efficient and effective infant-centered audio tagging of daylong audio recordings in home environments.
| Comments: | Accepted to Interspeech 2026 |
| Subjects: | Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.11587 [eess.AS] |
| (or arXiv:2608.11587v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2608.11587
arXiv-issued DOI via DataCite (pending registration)
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