pith. sign in

arxiv: 2606.13464 · v1 · pith:XGTLY47Dnew · submitted 2026-06-11 · 💻 cs.CL · cs.AI

Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversations

Xinxin Li , Huiyao Chen , Meishan Zhang , Yunxin Li , Zulong Chen , Zhibo Ren , Xiaoqing Dong Baotian Hu , Min Zhang This is my paper

Pith reviewed 2026-06-27 07:03 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords ASR correctionontology memorylong conversationscontext-dependent errorsdialogue historymemory augmentationspeech recognition
0
0 comments X

The pith

Ontology memory organizes conversation history into retrievable nodes to ground ASR corrections in long interleaved dialogues.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that long text-speech interactions create sparse, noisy correction evidence that raw history concatenation cannot reliably locate. It addresses this by turning preceding turns into a dynamic ontology memory whose nodes hold entities, terminology, surface variants, potential confusions, and semantic relations. Retrieval from these nodes supplies context for selective fixes instead of blanket edits. On a constructed long-range dataset the approach raises performance over direct correction in nine of ten backbone-setting pairs and produces more evidence-linked outputs.

Core claim

The framework organizes preceding interaction history into a dynamically updatable ontology memory, where entities, terminology, surface variants, potential ASR confusions, and semantic relations are stored as retrievable nodes for context-grounded correction.

What carries the argument

Ontology memory, a structured, updatable store of conversation nodes that can be queried to retrieve evidence for correcting context-dependent ASR errors.

If this is right

  • Correction accuracy rises over direct methods in nine of ten tested backbone and setting combinations.
  • Corrections shift toward selective, evidence-supported edits rather than ungrounded changes.
  • Context-dependent ASR errors in extended conversations become addressable through node retrieval.
  • The same memory structure supports ongoing updates as new turns arrive.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same node structure could be reused for related tasks such as entity tracking or intent resolution across long sessions.
  • Dynamic updates during live exchanges might allow the memory to track evolving terminology without full reprocessing.
  • Retrieval quality would determine whether gains hold when the underlying ASR backbone changes.

Load-bearing premise

Preceding interaction history can be reliably organized into a dynamically updatable ontology memory whose nodes supply usable evidence for correction when retrieved.

What would settle it

A controlled test on long interleaved dialogues in which retrieval from the ontology memory produces no measurable rise in correction accuracy or evidence grounding compared with direct use of raw history.

Figures

Figures reproduced from arXiv: 2606.13464 by Huiyao Chen, Meishan Zhang, Min Zhang, Xiaoqing Dong Baotian Hu, Xinxin Li, Yunxin Li, Zhibo Ren, Zulong Chen.

Figure 1
Figure 1. Figure 1: An example of context-grounded ASR cor￾rection in a long-range Chinese text-speech interleaved conversation. The original Chinese utterances are shown with English translations for readability. Earlier con￾text in the same conversation provides evidence for cor￾recting later ASR errors involving entities and domain￾specific terms. short voice commands. Instead, they are increas￾ingly embedded in continuous… view at source ↗
Figure 2
Figure 2. Figure 2: Overall architecture of the proposed ontology memory-augmented context-grounded ASR correction [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Edit behavior comparison under zero-shot and [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Impact of the text-input ratio on ASR correc [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Effect of the number of in-context examples [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
read the original abstract

Automatic speech recognition (ASR) correction has traditionally focused on isolated utterances or short local contexts. However, as text and speech become increasingly interleaved in long interactions, ASR correction requires conversation-level contextual evidence. Existing ASR correction methods often rely on the current hypothesis or concatenate raw dialogue history. In such contexts, sparse correction evidence can be difficult to locate amid redundancy and noise. Addressing these challenges, we propose an ontology memory-augmented ASR correction framework for long text-speech interleaved conversations. The framework organizes preceding interaction history into a dynamically updatable ontology memory, where entities, terminology, surface variants, potential ASR confusions, and semantic relations are stored as retrievable nodes for context-grounded correction. To evaluate this setting, we construct RAMC-Corr, a dataset derived from MAGIC-RAMC for long-range ASR correction with grounded context. Experiments on RAMC-Corr show that our method improves over direct correction in 9 out of 10 paired backbone-setting combinations and encourages more selective and evidence-grounded corrections for context-dependent ASR errors.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes an ontology memory-augmented ASR correction framework for long text-speech interleaved conversations. Preceding interaction history is organized into a dynamically updatable ontology memory whose nodes store entities, terminology, surface variants, potential ASR confusions, and semantic relations. The authors introduce the RAMC-Corr dataset derived from MAGIC-RAMC and report that the method improves over direct correction in 9 out of 10 paired backbone-setting combinations while producing more selective, evidence-grounded corrections for context-dependent errors.

Significance. If the empirical gains and the reliability of the ontology nodes are substantiated, the work could advance long-context ASR correction by replacing raw history concatenation with structured, retrievable evidence. The release of RAMC-Corr supplies a new benchmark resource for the community. The contribution is incremental rather than paradigm-shifting and hinges on whether the ontology extraction step functions as assumed.

major comments (2)
  1. [Abstract] Abstract: the central claim of improvement in 9/10 backbone-setting pairs is presented without any description of the experimental protocol, backbone models, baselines, error bars, statistical tests, ontology construction procedure, or retrieval mechanism, rendering the empirical result unevaluable.
  2. [Method] Method (ontology memory construction): the framework presupposes that entities, terminology, variants, confusions, and relations can be extracted and updated with sufficient fidelity from noisy ASR history to supply correct evidence on retrieval. No precision/recall metrics for node extraction, update error rates, or ablation that isolates the ontology structure versus raw history retrieval are supplied; this assumption is load-bearing for both the performance gains and the claim of more selective corrections.
minor comments (1)
  1. The notation used for ontology nodes and the retrieval scoring function should be introduced with a small concrete example early in the paper.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful comments, which highlight important aspects of clarity and validation. We address each major comment below, indicating the revisions we will make to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim of improvement in 9/10 backbone-setting pairs is presented without any description of the experimental protocol, backbone models, baselines, error bars, statistical tests, ontology construction procedure, or retrieval mechanism, rendering the empirical result unevaluable.

    Authors: We agree that the abstract lacks sufficient detail on the experimental setup to fully evaluate the claim. In the revised version, we will modify the abstract to briefly outline the backbone models (such as the specific ASR and LLM combinations tested), the direct correction baseline, the RAMC-Corr dataset, and indicate that the 9/10 improvement rate was consistent across settings with error bars reported in the main results. Details on ontology construction and retrieval are in the Method section, which we will cross-reference. revision: yes

  2. Referee: [Method] Method (ontology memory construction): the framework presupposes that entities, terminology, variants, confusions, and relations can be extracted and updated with sufficient fidelity from noisy ASR history to supply correct evidence on retrieval. No precision/recall metrics for node extraction, update error rates, or ablation that isolates the ontology structure versus raw history retrieval are supplied; this assumption is load-bearing for both the performance gains and the claim of more selective corrections.

    Authors: This is a valid concern regarding the core assumption. The manuscript presents end-to-end performance improvements and qualitative examples of selective corrections as supporting evidence. To directly address this, we will include in the revision an evaluation of the ontology node extraction quality using precision and recall on a subset of conversations with human annotations for entities and relations. Additionally, we will add an ablation study comparing our ontology memory approach to a baseline that retrieves from raw concatenated history, to isolate the benefit of the structured ontology. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical gains reported directly from experiments on constructed dataset

full rationale

The paper proposes an ontology memory framework for ASR correction and evaluates it empirically on the RAMC-Corr dataset derived from MAGIC-RAMC. No equations, fitted parameters, derivations, or predictions appear; the central claim (improvement in 9/10 backbone-setting pairs) is presented as an observed experimental outcome rather than a quantity defined in terms of the method itself or reduced via self-citation. The ontology construction step is an engineering assumption whose fidelity is not validated in the provided text, but this is a correctness/assumption risk rather than circularity. The derivation chain is self-contained as a standard ML method + ablation-style evaluation with no load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or external evidence for the ontology memory; the memory itself functions as the central invented component whose construction details are not described.

invented entities (1)
  • ontology memory no independent evidence
    purpose: Stores entities, terminology, surface variants, potential ASR confusions, and semantic relations from conversation history as retrievable nodes for context-grounded correction.
    Introduced as the core of the proposed framework; no independent evidence or falsifiable prediction outside the paper is mentioned.

pith-pipeline@v0.9.1-grok · 5733 in / 1190 out tokens · 23321 ms · 2026-06-27T07:03:49.657321+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

25 extracted references · 22 canonical work pages · 2 internal anchors

  1. [1]

    Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models

    Kim, Heeseung and Lee, Che Hyun and Park, Sangkwon and Yeom, Jiheum and Park, Nohil and Yu, Sangwon and Yoon, Sungroh. Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models. Findings of the Association for Computational Linguistics: ACL 2025. 2025. doi:10.18653/v1/2025.findings-acl.470

    work page doi:10.18653/v1/2025.findings-acl.470 2025

  2. [2]

    Connecting Automated Speech Recognition to Transcription Practices

    Billings, Blaine and McDonnell, Bradley. Connecting Automated Speech Recognition to Transcription Practices. Proceedings of the Eight Workshop on the Use of Computational Methods in the Study of Endangered Languages. 2025

    2025

  3. [3]

    MISP -Meeting: A Real-World Dataset with Multimodal Cues for Long-form Meeting Transcription and Summarization

    HangChen, HangChen and Yang, Chao-Han Huck and Gu, Jia-Chen and Siniscalchi, Sabato Marco and Du, Jun. MISP -Meeting: A Real-World Dataset with Multimodal Cues for Long-form Meeting Transcription and Summarization. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025. doi:10.18653/v1/2025.ac...

    work page doi:10.18653/v1/2025.acl-long.753 2025

  4. [4]

    S peech GPT : Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities

    Zhang, Dong and Li, Shimin and Zhang, Xin and Zhan, Jun and Wang, Pengyu and Zhou, Yaqian and Qiu, Xipeng. S peech GPT : Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities. Findings of the Association for Computational Linguistics: EMNLP 2023. 2023. doi:10.18653/v1/2023.findings-emnlp.1055

    work page doi:10.18653/v1/2023.findings-emnlp.1055 2023

  5. [5]

    AudioPaLM: A Large Language Model That Can Speak and Listen

    Paul K. Rubenstein and Chulayuth Asawaroengchai and Duc Dung Nguyen and Ankur Bapna and Zal. AudioPaLM:. CoRR , volume =. 2023 , url =. doi:10.48550/ARXIV.2306.12925 , eprinttype =. 2306.12925 , timestamp =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2306.12925 2023

  6. [6]

    Brouhaha: Multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimation,

    Chao. Generative Speech Recognition Error Correction With Large Language Models and Task-Activating Prompting , booktitle =. 2023 , url =. doi:10.1109/ASRU57964.2023.10389673 , timestamp =

    work page doi:10.1109/asru57964.2023.10389673 2023

  7. [7]

    HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models , booktitle =

    Chen Chen and Yuchen Hu and Chao. HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models , booktitle =. 2023 , url =

    2023

  8. [8]

    Marco Ruscone and Eirini Tsirvouli and Andrea Checcoli and D. NeKo:. PLoS Comput. Biol. , volume =. 2025 , url =. doi:10.1371/JOURNAL.PCBI.1013300 , timestamp =

    work page doi:10.1371/journal.pcbi.1013300 2025

  9. [9]

    C o V o GER : A Multilingual Multitask Benchmark for Speech-to-text Generative Error Correction with Large Language Models

    Yang, Zhengdong and Wan, Zhen and Li, Sheng and Yang, Chao-Han Huck and Chu, Chenhui. C o V o GER : A Multilingual Multitask Benchmark for Speech-to-text Generative Error Correction with Large Language Models. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. doi:10.18653/v1/2025.emnlp-main.320

    work page doi:10.18653/v1/2025.emnlp-main.320 2025

  10. [10]

    Anna V. R. Lattice Re-Scoring During Manual Editing for Automatic Error Correction of. 20th Annual Conference of the International Speech Communication Association, Interspeech 2019, Graz, Austria, September 15-19, 2019 , pages =. 2019 , url =. doi:10.21437/INTERSPEECH.2019-1790 , timestamp =

    work page doi:10.21437/interspeech.2019-1790 2019

  11. [11]

    Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation

    Ghosh, Sreyan and Rasooli, Mohammad Sadegh and Levit, Michael and Wang, Peidong and Xue, Jian and Manocha, Dinesh and Li, Jinyu. Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation. Findings of the Association for Computational Linguistics: ACL 2025. 2025. doi:10.18653/v1/2025.findings-acl.125

    work page doi:10.18653/v1/2025.findings-acl.125 2025

  12. [12]

    D e RAGEC : Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction

    Im, Solee and Lee, Wonjun and An, JinMyeong and Kim, Yunsu and Ok, Jungseul and Lee, Gary. D e RAGEC : Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction. Findings of the Association for Computational Linguistics: ACL 2025. 2025. doi:10.18653/v1/2025.findings-acl.786

    work page doi:10.18653/v1/2025.findings-acl.786 2025

  13. [13]

    Generative Annotation for ASR Named Entity Correction

    Luo, Yuanchang and Wei, Daimeng and Li, Shaojun and Shang, Hengchao and Guo, Jiaxin and Li, Zongyao and Wu, Zhanglin and Chen, Xiaoyu and Rao, Zhiqiang and Yang, Jinlong and Yang, Hao. Generative Annotation for ASR Named Entity Correction. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. doi:10.18653/v1/2025.em...

    work page doi:10.18653/v1/2025.emnlp-main.1052 2025

  14. [14]

    CoRR , volume =

    Fei Yang and Xuanfan Ni and Renyi Yang and Jiahui Geng and Qing Li and Chenyang Lyu and Yichao Du and Longyue Wang and Weihua Luo and Kaifu Zhang , title =. CoRR , volume =. 2026 , url =. doi:10.48550/ARXIV.2601.13539 , eprinttype =. 2601.13539 , timestamp =

    work page doi:10.48550/arxiv.2601.13539 2026

  15. [15]

    Whisper-CD: Accurate Long-Form Speech Recognition using Multi-Negative Contrastive Decoding

    Hoseong Ahn and Jeongyun Chae and Yoonji Park and Kyuhong Shim , title =. CoRR , volume =. 2026 , url =. doi:10.48550/ARXIV.2603.06193 , eprinttype =. 2603.06193 , timestamp =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2603.06193 2026

  16. [16]

    CoRR , volume =

    Haoqin Sun and Chenyang Lyu and Shiwan Zhao and Xuanfan Ni and Xiangyu Kong and Longyue Wang and Weihua Luo and Yong Qin , title =. CoRR , volume =. 2026 , url =. doi:10.48550/ARXIV.2602.05373 , eprinttype =. 2602.05373 , timestamp =

    work page doi:10.48550/arxiv.2602.05373 2026

  17. [17]

    arXiv preprint arXiv:2507.13264 , year=

    Mistral AI , title =. CoRR , volume =. 2025 , url =. doi:10.48550/ARXIV.2507.13264 , eprinttype =. 2507.13264 , timestamp =

    work page doi:10.48550/arxiv.2507.13264 2025

  18. [18]

    CoRR , volume =

    Chengyao Wang and Zhisheng Zhong and Bohao Peng and Senqiao Yang and Yuqi Liu and Haokun Gui and Bin Xia and Jingyao Li and Bei Yu and Jiaya Jia , title =. CoRR , volume =. 2025 , url =. doi:10.48550/ARXIV.2509.25131 , eprinttype =. 2509.25131 , timestamp =

    work page doi:10.48550/arxiv.2509.25131 2025

  19. [19]

    2024 , url =

    Kun Wei and Bei Li and Hang Lv and Quan Lu and Ning Jiang and Lei Xie , title =. 2024 , url =. doi:10.1109/TASLP.2024.3389630 , timestamp =

    work page doi:10.1109/taslp.2024.3389630 2024

  20. [20]

    Flexibly Utilize Memory for Long-Term Conversation via a Fragment-then-Compose Framework

    Ke, Cai and Du, Yiming and Liang, Bin and Xiang, Yifan and Gui, Lin and Li, Zhongyang and Wang, Baojun and Yu, Yue and Wang, Hui and Wong, Kam-Fai and Xu, Ruifeng. Flexibly Utilize Memory for Long-Term Conversation via a Fragment-then-Compose Framework. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. doi:10.18...

    work page doi:10.18653/v1/2025.emnlp-main.1069 2025

  21. [21]

    Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations

    Chen, Nuo and Li, Hongguang and Chang, Jianhui and Huang, Juhua and Wang, Baoyuan and Li, Jia. Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations. Proceedings of the 31st International Conference on Computational Linguistics. 2025

    2025

  22. [22]

    In2023 IEEE International Conference on Big Data (BigData)

    Min Zhang and Xiaosong Qiao and Yanqing Zhao and Chang Su and Yinglu Li and Yuang Li and Ming Zhu and Mengyao Piao and Song Peng and Shimin Tao and Hao Yang and Yanfei Jiang , editor =. Knowledge Prompt for Whisper: An. 2023 , url =. doi:10.1109/BIGDATA59044.2023.10386366 , timestamp =

    work page doi:10.1109/bigdata59044.2023.10386366 2023

  23. [23]

    CoRR , volume =

    Sreyan Ghosh and Mohammad Sadegh Rasooli and Michael Levit and Peidong Wang and Jian Xue and Dinesh Manocha and Jinyu Li , title =. CoRR , volume =. 2024 , url =. doi:10.48550/ARXIV.2410.13198 , eprinttype =. 2410.13198 , timestamp =

    work page doi:10.48550/arxiv.2410.13198 2024

  24. [24]

    Open Source MagicData-RAMC:

    Zehui Yang and Yifan Chen and Lei Luo and Runyan Yang and Lingxuan Ye and Gaofeng Cheng and Ji Xu and Yaohui Jin and Qingqing Zhang and Pengyuan Zhang and Lei Xie and Yonghong Yan , editor =. Open Source MagicData-RAMC:. 23rd Annual Conference of the International Speech Communication Association, Interspeech 2022, Incheon, Korea, September 18-22, 2022 , ...

    work page doi:10.21437/interspeech.2022-729 2022

  25. [25]

    Efficient memory management for large language model serving with pagedattention

    Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph Gonzalez and Hao Zhang and Ion Stoica , editor =. Efficient Memory Management for Large Language Model Serving with PagedAttention , booktitle =. 2023 , url =. doi:10.1145/3600006.3613165 , timestamp =

    work page doi:10.1145/3600006.3613165 2023