mlsanityData to 14 Oct 2026

AP-GRPO: Anchor-Gated Phonetic Alignment with Policy Optimization for Pathological Speech Reconstruction

arXiv 2606.15540 ↗First posted 14 Jun 2026cs.SD cs.AI cs.MMcited by 0

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Abstract (arXiv)

Pathological speech from patients with neurodegenerative and neuromotor disorders is often acoustically distorted and linguistically fragmented, making pathological speech reconstruction necessary to recover intended textual content from distorted and incomplete speech recordings. Crucially, such recordings are rarely uniformly degraded: some words or short phrases remain reliable and can serve as audible anchors for reconstructing the corrupted surrounding content. We introduce Anchor-gated Phonetic Group Relative Policy Optimization (AP-GRPO), a GRPO framework with phonetic reward that aligns speech language models (SLMs) through audible-anchor preservation and inter-anchor phonetic compatibility to the original speech signal. AP-GRPO consists of: (i) an anchor-gated reward that matches reliable audible anchors in clear regions; and (ii) an inter-anchor phonetic alignment reward that evaluates whether recovered contents are phonetically supported by the corresponding corrupted inter-anchor speech span. Across four disease conditions, AP-GRPO improves faithful speech reconstruction, and the learned anchor constraint automatically adapts to each condition and thus reveals interpretable disease-specific profiles: conditions with severe articulatory degradation require stronger anchor enforcement, whereas milder impairment or linguistically impaired conditions rely more on phonetic alignment for inter-anchor recovery.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 14 Jun 2026.

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References · 17

  1. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  3. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  4. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  5. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
  6. 2023WhisperX: Time-Accurate Speech Transcription of Long-Form Audiocited by 326
  7. 2017Soft-DTW: a Differentiable Loss Function for Time-Seriescited by 271
  8. 2021Detecting cognitive decline using speech only: The ADReSSo Challengecited by 81
  9. 2024SpeechAlign: Aligning Speech Generation to Human Preferencescited by 47
  10. 2024Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedbackcited by 23
  11. 2024Training Data Augmentation for Dysarthric Automatic Speech Recognition by Text-to-Dysarthric-Speech Synthesiscited by 14
  12. 2024UNIT-DSR: Dysarthric Speech Reconstruction System Using Speech Unit Normalizationcited by 8
5 more
  1. 2025DiffDSR: Dysarthric Speech Reconstruction Using Latent Diffusion Modelcited by 5
  2. 2024CoLM-DSR: Leveraging Neural Codec Language Modeling for Multi-Modal Dysarthric Speech Reconstructioncited by 4
  3. 2025Exploring Generative Error Correction for Dysarthric Speech Recognitioncited by 3
  4. 2026DemMA: Dementia Multi-Turn Dialogue Agent with Expert-Guided Reasoning and Action Simulationcited by 2
  5. 2020Deep segmental phonetic posterior-grams based discovery of non-categories in L2 English speechcited by 1

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  6. 2025Learning to Align, Aligning to Learn: A Unified Approach for Self-Optimized Alignment
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