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Characterisation of voice quality of Parkinson's disease using differential phonological posterior features.

, , , , , and . Computer Speech & Language, (2017)

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Characterisation of voice quality of Parkinson's disease using differential phonological posterior features., , , , , and . Computer Speech & Language, (2017)Apkinson - A Mobile Monitoring Solution for Parkinson's Disease., , , , , and . INTERSPEECH, page 1839-1843. ISCA, (2017)Multimodal I-vectors to Detect and Evaluate Parkinson's Disease., , , and . INTERSPEECH, page 2349-2353. ISCA, (2018)Empirical Mode Decomposition articulation feature extraction on Parkinson's Diadochokinesia., , , , and . Comput. Speech Lang., (2022)Language Independent Assessment of Motor Impairments of Patients with Parkinson's Disease Using i-Vectors., , , , and . TSD, volume 10415 of Lecture Notes in Computer Science, page 147-155. Springer, (2017)Speaker Model to Monitor the Neurological State and the Dysarthria Level of Patients with Parkinson's Disease., , , , and . TSD, volume 10415 of Lecture Notes in Computer Science, page 272-280. Springer, (2017)Time Dependent ARMA for Automatic Recognition of Fear-Type Emotions in Speech., , , , , and . TSD, volume 9302 of Lecture Notes in Computer Science, page 96-104. Springer, (2015)Gender-dependent GMM-UBM for tracking Parkinson's disease progression from speech., , , , , and . ITG Symposium on Speech Communication, page 1-5. IEEE, (2016)Emotion recognition from speech under environmental noise conditions using wavelet decomposition., , , , , and . ICCST, page 247-252. IEEE, (2015)Parkinson's Disease and Aging: Analysis of Their Effect in Phonation and Articulation of Speech., , and . Cognitive Computation, 9 (6): 731-748 (2017)