{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T08:33:21Z","timestamp":1788165201325,"version":"build-2803163510"},"reference-count":59,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"crossref","award":["2022M3A9B6082791"],"award-info":[{"award-number":["2022M3A9B6082791"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Early, non-invasive detection of Parkinson\u2019s Disease (PD) using speech analysis offers promise for scalable screening. In this work, we propose a multi-channel spectro-temporal deep-learning approach for PD detection from sentence-level speech, a clinically relevant yet underexplored modality. We extract and fuse three complementary time\u2013frequency representations\u2014mel spectrogram, constant-Q transform (CQT), and gammatone spectrogram\u2014into a three-channel input analogous to an RGB image. This fused representation is evaluated across CNNs (ResNet, DenseNet, and EfficientNet) and Vision Transformer using the PC-GITA dataset, under 10-fold subject-independent cross-validation for robust assessment. Results showed that fusion consistently improves performance over single representations across architectures. EfficientNet-B2 achieves the highest accuracy (84.39% \u00b1 5.19%) and F1-score (84.35% \u00b1 5.52%), outperforming recent methods using handcrafted features or pretrained models (e.g., Wav2Vec2.0, HuBERT) on the same task and dataset. Performance varies with sentence type, with emotionally salient and prosodically emphasized utterances yielding higher AUC, suggesting that richer prosody enhances discriminability. Our findings indicate that multi-channel fusion enhances sensitivity to subtle speech impairments in PD by integrating complementary spectral information. Our approach implies that multi-channel fusion could enhance the detection of discriminative acoustic biomarkers, potentially offering a more robust and effective framework for speech-based PD screening, though further validation is needed before clinical application.<\/jats:p>","DOI":"10.3390\/jimaging11100341","type":"journal-article","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T07:58:13Z","timestamp":1759305493000},"page":"341","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-Channel Spectro-Temporal Representations for Speech-Based Parkinson\u2019s Disease Detection"],"prefix":"10.3390","volume":"11","author":[{"given":"Hadi Sedigh","family":"Malekroodi","sequence":"first","affiliation":[{"name":"Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7982-1036","authenticated-orcid":false,"given":"Nuwan","family":"Madusanka","sequence":"additional","affiliation":[{"name":"Digital Healthcare Research Center, College of Information Technology and Convergence, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1574-7145","authenticated-orcid":false,"given":"Byeong-il","family":"Lee","sequence":"additional","affiliation":[{"name":"Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Republic of Korea"},{"name":"Digital Healthcare Research Center, College of Information Technology and Convergence, Pukyong National University, Busan 48513, Republic of Korea"},{"name":"Major of Human Bioconvergence, Division of Smart Healthcare, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4864-959X","authenticated-orcid":false,"given":"Myunggi","family":"Yi","sequence":"additional","affiliation":[{"name":"Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Republic of Korea"},{"name":"Digital Healthcare Research Center, College of Information Technology and Convergence, Pukyong National University, Busan 48513, Republic of Korea"},{"name":"Major of Biomedical Engineering, Division of Smart Healthcare, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1016\/j.neubiorev.2016.07.010","article-title":"Motor Symptoms in Parkinson\u2019s Disease: A Unified Framework","volume":"68","author":"Moustafa","year":"2016","journal-title":"Neurosci. 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