From Oracle to Noisy Context: Mitigating Contextual Exposure Bias in Speech-LLMs
Contextual automatic speech recognition (ASR) with Speech-LLMs is typically trained with oracle conversation history, but relies on error-prone history at inference, causing a train-test mismatch in the context channel that we term contextual exposure bias. We propose a unified training framework to improve robustness under realistic histories: (i) Teacher Error Knowledge by using Whisper large-v3 hypotheses as training-time history, (ii) Context Dropout to regularize over-reliance on history, and (iii) Direct Preference Optimization (DPO) on curated failure cases. Experiments on TED-LIUM 3 (in-domain) and zero-shot LibriSpeech (out-of-domain) show consistent gains under predicted-history...
| Authors | Xiaoyong Guo, Nanjie Li, Zijie Zeng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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