Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning

Xulin Fan; Jialu Li; Mohammad Nur Hossain Khan; Kexin Hu; Bashima Islam; Mark Hasegawa-Johnson; Nancy L. McElwain

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arXiv · 2608.11587

Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning

Xulin Fan·Jialu Li·Mohammad Nur Hossain Khan·Kexin Hu·Bashima Islam·Mark Hasegawa-Johnson·Nancy L. McElwain

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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.

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Xulin Fan, Jialu Li, Mohammad Nur Hossain Khan, Kexin Hu, Bashima Islam, Mark Hasegawa-Johnson, Nancy L. McElwain. 2026-08-12. Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning. https://arxiv.org/abs/2608.11587

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