Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.14479

AphaVoice: Text-to-Speech Model for Aphasia Patient Simulation

Al Varrel Putra Kusuma·Fernando Bello·Joshua Brown
View original source ↗Read source document ↗Export metadata ↓

Abstract

While language therapy is the primary treatment for post-stroke aphasia, limited clinical resources often restrict patient access to proper care. Addressing this gap requires reliable training simulations for medical professionals. However, current AI efforts focus almost exclusively on speech processing, leaving speech generation largely unexplored. This study introduces AphaVoice, a novel text-to-speech (TTS) framework explicitly engineered to synthesize highly realistic aphasic speech. Evaluated against state-of-the-art TTS systems, AphaVoice demonstrates a superior ability to capture the clinical feature of dysfluency, outperforming existing models in temporal metrics while consistently maintaining speaker identity. By leveraging latent space shifting, the model also supports multi-speaker generation without sacrificing phonetic realism. Ultimately, AphaVoice provides a scalable, high-fidelity tool for medical training and clinical simulation related to aphasia.

Explore related subjects

cs.CLcs.AI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Al Varrel Putra Kusuma, Fernando Bello, Joshua Brown. 2026-10-10. AphaVoice: Text-to-Speech Model for Aphasia Patient Simulation. https://arxiv.org/abs/2610.14479

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Post-Training Large Language Models via Reinforcement Learning from Self-Feedback

Large Language Models (LLMs) often produce plausible but poorly-calibrated answers, limiting their reliability on reasoning-intensive tasks. Recent research suggests that Chain-of-Thought (CoT) reasoning paths are inherent in pre-trained LLMs and can be elicited by simply altering the decoding process, where the presence of a CoT path correlates with higher answer confidence. Building on these insights, we present Reinforcement Learning from Self-Feedback (RLSF), a post-training stage that utilises the model's intrinsic confidence as a self-generated reward. By generating multiple CoT decoding beams from a frozen LLM, we compute the confidence of each final answer span and rank the resulting traces accordingly to create synthetic preferences. These preferences are subsequently utilised to fine-tune the policy through standard preference optimisation, requiring no human labels, gold answers, or externally curated rewards. RLSF simultaneously (i) refines the model's probability estimates--restoring well-behaved calibration--and (ii) strengthens step-by-step reasoning, yielding improved performance on arithmetic reasoning and multiple-choice question answering. By converting a model's own uncertainty into structured self-feedback, RLSF affirms reinforcement learning on intrinsic model behaviour as a principled and data-efficient component of the LLM post-training pipeline. Our results demonstrate that leveraging these inherent reasoning capabilities provides a robust path for enhancing model reliability without manual prompt engineering or external supervision.

cs.CL↗
arXiv2026

Majority Bit-Aware Watermarking For Large Language Models

The growing deployment of Large Language Models (LLMs) has raised concerns about their misuse in generating harmful or deceptive content. To address this issue, watermarking methods have been proposed to embed identifiable multi-bit messages into generated text for misuse tracing. However, existing methods often suffer from a fundamental trade-off between text quality and decoding accuracy. In particular, they have to restrict the size of the preferred token set (i.e., green list) during encoding to maintain a detectable watermark signal for decoding, which inevitably degrades generation quality. To improve this trade-off, we propose a novel message encoding paradigm called \textit{majority bit-aware encoding}, which relaxes the watermark signal strength from the green list size. This strategy allows for a strong watermark signal to be preserved in generated texts even when using a large green list. We introduce two instantiations of this paradigm: MajorMark and MajorMark$^{+}$, where the latter is specifically optimized for long messages. Extensive experiments on state-of-the-art LLMs demonstrate that our methods achieve higher decoding accuracy and superior text quality compared to prior baselines. The code is at https://github.com/JiiahaoXU/MajorMark.

cs.CL↗
arXiv2026

Demasking Fine-Tuning for Knowledge Injection in Autoregressive and Diffusion LLMs

Large language models (LLMs) are often used in environments where facts evolve, yet factual knowledge updates via fine-tuning on unstructured plain text often suffer from 1) reliance on paraphrasing augmentation, without which training does not generalize to downstream QA tasks, and 2) the reversal curse. Recent evidence of the sample efficiency of masked diffusion large language models (dLLMs) and the bidirectional information flow in their demasking training objective suggests that the demasking objective may facilitate knowledge learning from raw knowledge text. We test this hypothesis in controlled knowledge fine-tuning experiments on dLLMs and autoregressive LLMs (arLLMs) and find that while arLLMs rely on paraphrase augmentation to generalize knowledge text into question-answering (QA) capability, dLLMs do not require paraphrases to achieve high QA accuracy. We find that giving arLLMs a demasking objective, where the LLM must reconstruct the original text from a masked copy given in context, improves the efficacy of knowledge injection, removing the need for paraphrases and mitigating the reversal curse, recovering most of the gap between arLLMs and dLLMs. We also provide preliminary evidence of the broader applicability of using the demasking objective in arLLM fine-tuning on a large-scale knowledge-intensive dataset.

cs.CL↗