{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T02:12:33Z","timestamp":1784945553834,"version":"3.55.0"},"reference-count":49,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Introduction: Dysarthria, a motor speech disorder caused by neurological damage, significantly hampers speech intelligibility, creating communication barriers for affected individuals. Voice conversion (VC) systems have been developed to address this, yet accurately predicting phonemes in dysarthric speech remains a challenge due to its variability. This study proposes a novel approach that integrates Fuzzy Expectation Maximization (FEM) with diffusion models for enhanced phoneme prediction, aiming to improve the quality of dysarthric voice conversion. Methods: The proposed method combines FEM clustering with Diffusion Probabilistic Models (DPM). Diffusion models simulate noise addition and removal to enhance the robustness of speech signals, while FEM iteratively optimizes phoneme boundaries, reducing uncertainty. The system was trained using the Saarland University Voice Disorder dataset, consisting of dysarthric and normal speech samples, with the conversion process represented in the Mel-spectrogram domain. The framework employs both subjective (Mean Opinion Score, MOS) and objective (Word Error Rate, WER) metrics for evaluation, complemented by ablation studies. Results: Experimental results showed that the proposed method significantly improved phoneme prediction accuracy and overall voice conversion quality. It achieved higher MOSs for naturalness, intelligibility, and speaker similarity compared to existing models like StarGAN-VC and CycleGAN-VC. Additionally, the proposed method demonstrated a lower WER for both mild and severe dysarthria cases, indicating better performance in producing intelligible speech. Discussion: The integration of FEM with diffusion models offers substantial improvements in handling the irregularities of dysarthric speech. The method\u2019s robustness, as evidenced by the ablation studies, shows that it can maintain speech naturalness and intelligibility even without a speaker-encoder. These findings suggest that the proposed approach can contribute to the development of more reliable assistive communication technologies for individuals with dysarthria, providing a promising foundation for future advancements in personalized speech therapy.<\/jats:p>","DOI":"10.3390\/diagnostics14232693","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T08:24:14Z","timestamp":1732868654000},"page":"2693","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing Dysarthric Voice Conversion with Fuzzy Expectation Maximization in Diffusion Models for Phoneme Prediction"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7504-9484","authenticated-orcid":false,"given":"Wen-Shin","family":"Hsu","sequence":"first","affiliation":[{"name":"Department of Medical Information, Chung Shan Medical University, Taichung 402201, Taiwan"},{"name":"Informatics Office Technology, Chung Shan Medical University Hospital, Taichung 402201, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang-Tao","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Medical Information, Chung Shan Medical University, Taichung 402201, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1297-4825","authenticated-orcid":false,"given":"Wei-Hsun","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 402202, Taiwan"},{"name":"Department of Golden-Ager Industry Management, Chaoyang University of Technology, Taichung 413310, Taiwan"},{"name":"Department of Orthopedic Surgery, Changhua Christian Hospital, Changhua 500209, Taiwan"},{"name":"Department of Medical Imaging and Radiology, Shu-Zen Junior College of Medicine and Management, Kaohsiung 82100, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/B978-0-444-52901-5.00022-8","article-title":"Disorders of communication: Dysarthria","volume":"110","author":"Enderby","year":"2013","journal-title":"Handb. 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