Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning
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 ...
| Authors | Xulin Fan, Jialu Li, Mohammad Nur Hossain Khan et al. |
|---|---|
| Primary category | eess.AS |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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