arXiv Computation and Language
Sep 22

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

arXiv:2604.06487v2 Announce Type: replace Abstract: Conventional end-to-end automatic speech recognition (ASR) systems rely on paired speech-text data for domain adaptation. Recent LLM-based ASR arch...

By Thibault Ba\~neras-Roux, Sergio Burdisso, Esa\'u Villatoro-Tello, Dairazalia S\'anchez-Cort\'es, Shiran Liu, Severin Baroudi, Shashi Kumar, Hasindri Watawana, Manjunath K E, Kadri Hacioglu, Petr Motlicek, Andreas Stolcke
arXiv Computation and Language
3d ago

Exploring the potential and limitations of Model Merging for Multi-Domain Adaptation in ASR

The paper investigates model merging as a scalable alternative to multi-task training for multi-domain automatic speech recognition (ASR). It benchmarks 11 merging algorithms across 10 European Portuguese domains, assessing in‑domain accuracy, robustness to distribution shift, and performance on English and multilingual data. The authors introduce BoostedTSV‑M, a new merging method that mitigates rank collapse through singular‑value boosting, achieving better performance than full fine‑tuning while maintaining out‑of‑distribution generalisation in a single model.

By Carlos Carvalho, Francisco Teixeira, Thomas Rolland, Alberto Abad
arXiv AI
Sep 4

Listen to the Latents: Self-Correcting Speech Recognition in Large Audio Language Models Through Hidden-State Interactions

The paper introduces Hybrid Search, a method that refines warm-initialized large language model (LLM) based automatic speech recognition (ASR) systems by exploiting interactions between ASR hidden states and the base LLM’s hidden states. By identifying tokens with high semantic dependence and selectively correcting them, the approach surpasses traditional global LLM‑correction techniques such as rescoring and late fusion. The study demonstrates that even after warm initialization, LLM‑based ASR models can further benefit from their base LLM during inference.

By Chan-Jan Hsu, Jaeyeon Kim, Chao-Han Huck Yang, Shinji Watanabe, Hung-yi Lee, Carlos Busso