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

, , , , , and . Comput. Speech Lang., (2017)

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Comparison of User Models Based on GMM-UBM and I-Vectors for Speech, Handwriting, and Gait Assessment of Parkinson's Disease Patients., , , and . ICASSP, page 6544-6548. IEEE, (2020)Transfer learning helps to improve the accuracy to classify patients with different speech disorders in different languages., , , , , , and . Pattern Recognit. Lett., (2021)One System to Rule them All: a Universal Intent Recognition System for Customer Service Chatbots., , , , and . CoRR, (2021)Representation Learning Strategies to Model Pathological Speech: Effect of Multiple Spectral Resolutions., , , and . CoRR, (2022)Acoustic and Linguistic Analyses to Assess Early-Onset and Genetic Alzheimer's Disease., , , , , , , , , and 3 other author(s). ICASSP, page 8338-8342. IEEE, (2021)End-2-End Modeling of Speech and Gait from Patients with Parkinson's Disease: Comparison Between High Quality Vs. Smartphone Data., , , , , and . ICASSP, page 7298-7302. IEEE, (2021)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)Characterisation of voice quality of Parkinson's disease using differential phonological posterior features., , , , , and . Comput. Speech Lang., (2017)Novel Speech Recognition Systems Applied to Forensics within Child Exploitation: Wav2vec2.0 vs. Whisper., and . Sensors, 23 (4): 1843 (February 2023)