Thibault Bañeras-Roux

Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses

Large language models can post-edit ASR by using linguistic context to compare N-best hypotheses and rewrite the transcript when needed, often improving WER without retraining the recognizer. However, prompt-only correction is brittle: it may over-edit when top-1 is correct and hallucinate when the reference is absent,...

Context Projector: Complementary Keyword and Dialogue Context Embeddings for LLM-based ASR

Closing the Speech-Text Gap with Limited Audio for Effective Domain Adaptation in LLM-Based ASR

MB using only 10% of the target-domain speech achieves word error rates comparable to, or better than, conventional ASR fine-tuning with the full dataset, indicating that small amounts of speech provide a strong modality-alignment signal.

Generative vs. Encoder Large Language Models for ASR Evaluation: A Comparative Study

The results show that encoder-based metrics remain highly competitive, while generative LLMs perform strongly in hypothesis comparison and improve the interpretability of ASR evaluation.