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News/Graph-Based Phonetic Error Correction of Noisy ASR
arxiv
PublishedJune 25, 2026 at 4:00 AM
—neutral

Graph-Based Phonetic Error Correction of Noisy ASR

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.24889v1 Announce Type: new 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 a

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Discussion
Mentioned models
04
  • 01
    G-SPIN
  • 02
    graph neural network (GNN)
  • 03
    masked language model (MLM)
  • 04
    instruction-tuned large language model (LLM)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#asr#speech-recognition#nlp#language-models

No replies yet. Be first.

Mentioned models
04
  • 01
    G-SPIN
  • 02
    graph neural network (GNN)
  • 03
    masked language model (MLM)
  • 04
    instruction-tuned large language model (LLM)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#asr#speech-recognition#nlp#language-models

Related coverage

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arxivWhen Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems14harxivQuery-Driven Learning for Lifted Reasoning14harxivThe Social World Model Evaluation Gap14h
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