Hernandez A, Arias Vergara T, Maier A, Perez Toro PA (2026)
Publication Language: English
Publication Type: Conference contribution, Original article
Publication year: 2026
Publisher: IEEE
City/Town: Barcelona, Spain
Pages Range: 18447-18451
Conference Proceedings Title: Proceedings of the 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISBN: 979-8-3315-6701-9
DOI: 10.1109/ICASSP55912.2026.11463078
We investigate the use of large language models (LLMs) as post-processing modules for automatic speech recognition (ASR), focusing on their ability to perform error correction for disordered speech. In particular, we propose confidence-informed prompting, where word-level uncertainty estimates are embedded directly into LLM training to improve robustness and generalization across speakers and datasets. This approach directs the model to uncertain ASR regions and reduces overcorrection. We fine-tune a LLaMA 3.1 model and compare our approach to both transcript-only fine-tuning and post hoc confidence-based filtering. Evaluations show that our method achieves a 10% relative WER reduction compared to naive LLM correction on the Speech Accessibility Project spontaneous speech and a 47% reduction on TORGO, demonstrating the effectiveness of confidence-aware fine-tuning for impaired speech.
APA:
Hernandez, A., Arias Vergara, T., Maier, A., & Perez Toro, P.A. (2026). Confidence-guided error correction for disordered speech recognition. In Proceedings of the 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 18447-18451). Barcelona, Spain: IEEE.
MLA:
Hernandez, Abner, et al. "Confidence-guided error correction for disordered speech recognition." Proceedings of the Proceedings of the 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Barcelona, Spain: IEEE, 2026. 18447-18451.
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