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Application of Nonlinear Dynamics Characterization to Emotional Speech.

, , , , and . NOLISP, volume 7015 of Lecture Notes in Computer Science, page 127-136. Springer, (2011)

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New Spanish speech corpus database for the analysis of people suffering from Parkinson's disease., , , , and . LREC, page 342-347. European Language Resources Association (ELRA), (2014)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)A machine learning perspective on the emotional content of Parkinsonian speech., , , , and . Artif. Intell. Medicine, (2021)Representation Learning Strategies to Model Pathological Speech: Effect of Multiple Spectral Resolutions., , , and . CoRR, (2022)Transfer learning helps to improve the accuracy to classify patients with different speech disorders in different languages., , , , , , and . Pattern Recognit. Lett., (2021)Non-negative matrix factorization-based time-frequency feature extraction of voice signal for Parkinson's disease prediction., , , and . Comput. Speech Lang., (2021)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)Analysis of speech of people with Parkinson's disease.. University of Erlangen-Nuremberg, Germany, (2016)Multimodal I-vectors to Detect and Evaluate Parkinson's Disease., , , and . INTERSPEECH, page 2349-2353. ISCA, (2018)