@inproceedings{zhang-etal-2026-ap,
title = "{AP}-{GRPO}: Anchor-Gated Phonetic Alignment with Policy Optimization for Pathological Speech Reconstruction",
author = "Zhang, Pengfei and
Nguyen, Hoang H and
Song, Yutong and
Huang, Wenjun and
Imu, Tahmid Imtiaz and
Zou, Henry Peng and
Wu, Jiang and
Xu, Honghui and
Rahmani, Amir M.",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1287/",
pages = "29320--29340",
ISBN = "979-8-89176-459-0",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T AP-GRPO: Anchor-Gated Phonetic Alignment with Policy Optimization for Pathological Speech Reconstruction
%A Zhang, Pengfei
%A Nguyen, Hoang H.
%A Song, Yutong
%A Huang, Wenjun
%A Imu, Tahmid Imtiaz
%A Zou, Henry Peng
%A Wu, Jiang
%A Xu, Honghui
%A Rahmani, Amir M.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhang-etal-2026-ap
%X 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.
%U https://aclanthology.org/2026.emnlp-main.1287/
%P 29320-29340
Markdown (Informal)
[AP-GRPO: Anchor-Gated Phonetic Alignment with Policy Optimization for Pathological Speech Reconstruction](https://aclanthology.org/2026.emnlp-main.1287/) (Zhang et al., EMNLP 2026)
ACL
- Pengfei Zhang, Hoang H Nguyen, Yutong Song, Wenjun Huang, Tahmid Imtiaz Imu, Henry Peng Zou, Jiang Wu, Honghui Xu, and Amir M. Rahmani. 2026. AP-GRPO: Anchor-Gated Phonetic Alignment with Policy Optimization for Pathological Speech Reconstruction. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 29320–29340, Budapest, Hungary. Association for Computational Linguistics.