Graph-Based Phonetic Error Correction of Noisy ASR
Researchers propose G-SPIN, a structured ASR correction framework that combines phonetic graph modeling with contextual language understanding. It uses a graph neural network to generate acoustically plausible candidate sets, a masked language model for scoring, and an instruction-tuned large language model for final re-ranking, enabling lightweight, modular inference-time correction.
[2606.24889] Graph-Based Phonetic Error Correction of Noisy ASR
[Submitted on 29 Apr 2026]
Title:Graph-Based Phonetic Error Correction of Noisy ASR
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Abstract:Automatic speech recognition (ASR) systems, despite low overall word error rates, produce residual lexical errors that disproportionately affect semantically critical tokens such as named entities, negations, and sentiment-bearing words. These errors are often structured, arising from phonetic similarity rather than random noise, making naive token-level correction insufficient. We propose a structured ASR correction framework, that we call G-SPIN, that combines phonetic graph modeling with contextual language understanding. A graph neural network (GNN) first constructs acoustically plausible candidate neighborhoods for flagged tokens, explicitly restricting the correction search space to phonetic alternatives. A masked language model (MLM) then provides local contextual scoring, and an instruction-tuned large language model (LLM) performs final context-aware re-ranking over this compact candidate set. By decoupling structured phonetic reasoning from contextual semantic selection, our method avoids unconstrained generation while improving correction accuracy. The framework is lightweight, modular, and operates entirely at inference time.
Comments: Accepted at ACL Industry Track 2026
Subjects:
Computation and Language (cs.CL); Sound (cs.SD)
Cite as: arXiv:2606.24889 [cs.CL]
(or arXiv:2606.24889v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.24889
arXiv-issued DOI via DataCite
Submission history
From: Mohammadi Zaki [view email] [v1] Wed, 29 Apr 2026 13:57:11 UTC (392 KB)
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