%Aigaion2 BibTeX export from Idiap Publications
%Saturday 21 February 2026 01:08:37 AM

@INPROCEEDINGS{Burdisso_ICASSP2026_2026,
                      author = {Burdisso, Sergio and Villatoro-Tello, Esa{\'{u}} and Carofilis, Andr{\'{e}}s and Kumar, Shashi and Hacioğlu, Kadri and Madikeri, Srikanth and Rangappa, Pradeep and E, Manjunath K and Motlicek, Petr and Venkatesan, Shankar and Stolcke, Andreas},
                    projects = {Idiap, UNIPHORE, ELOQUENCE},
         mainresearchprogram = {AI for Everyone},
  additionalresearchprograms = {AI for Everyone},
                       title = {Text-only adaptation in LLM-based ASR through text denoising},
                   booktitle = {ICASSP},
                        year = {2026},
                    abstract = {Adapting automatic speech recognition (ASR) systems based on large language models (LLMs) to new domains using text-only data is a significant yet underexplored challenge. Standard fine-tuning of the LLM on target-domain text often disrupts the critical alignment between speech and text modalities learned by the projector, degrading performance. We introduce a novel text-only adaptation method that emulates the audio projection task by treating it as a text denoising task. Our approach thus trains the LLM to recover clean transcripts from noisy inputs. This process effectively adapts the model to a target domain while preserving cross-modal alignment. Our solution is lightweight, requiring no architectural changes or additional parameters. Extensive evaluation on two datasets demonstrates up to 22.1\% relative improvement, outperforming recent state-of-the-art text-only adaptation methods.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2026/Burdisso_ICASSP2026_2026.pdf}
}