Automatic speech recognition (ASR) degrades severely in noisy environments. Although speech enhancement (SE) front-ends effectively suppress background noise, they often introduce artifacts that harm recognition. Observation addition (OA) addressed this issue by fusing noisy and SE enhanced speech, improving recognition without modifying the parameters of the SE or ASR models. This paper proposes an intelligibility-guided OA method, where fusion weights are derived from intelligibility estimates obtained directly from the backend ASR. Unlike prior OA methods based on trained neural predictors, the proposed method is training-free, reducing complexity and enhances generalization. Extensive experiments across diverse SE-ASR combinations and datasets demonstrate strong robustness and improvements over existing OA baselines. Additional analyses of intelligibility-guided switching-based alternatives and frame versus utterance-level OA further validate the proposed design.
@article{arxiv.2602.20967,
title = {Training-Free Intelligibility-Guided Observation Addition for Noisy ASR},
author = {Haoyang Li and Changsong Liu and Wei Rao and Hao Shi and Sakriani Sakti and Eng Siong Chng},
journal= {arXiv preprint arXiv:2602.20967},
year = {2026}
}