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Transfer Learning to Detect Parkinson's Disease from Speech In Different Languages Using Convolutional Neural Networks with Layer Freezing.

C. Rios-Urrego, J. Vásquez-Correa, J. Orozco-Arroyave, and E. Nöth. TDS, volume 12284 of Lecture Notes in Computer Science, page 331-339. Springer, (2020)

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Elmar Nöth

Ludwig Nöth

Stefan Nöth

Other publications of authors with the same name

On the Automatic Classification of Reading Disorders.A. Maier, C. Parchmann, T. Bocklet, F. Hönig, O. Kratz, S. Horndasch, E. Nöth, and G. Moll. PRIS, page 18-27. INSTICC PRESS, (2009)Transfer learning helps to improve the accuracy to classify patients with different speech disorders in different languages.J. Vásquez-Correa, C. Rios-Urrego, T. Arias-Vergara, M. Schuster, J. Rusz, E. Nöth, and J. Orozco-Arroyave. Pattern Recognit. Lett., (2021)New Spanish speech corpus database for the analysis of people suffering from Parkinson's disease.J. Orozco-Arroyave, J. Arias-Londoño, J. Vargas-Bonilla, M. Gonzalez-Rátiva, and E. Nöth. LREC, page 342-347. European Language Resources Association (ELRA), (2014)Empirical Mode Decomposition articulation feature extraction on Parkinson's Diadochokinesia.A. Rueda, J. Vásquez-Correa, J. Orozco-Arroyave, E. Nöth, and S. Krishnan. Comput. Speech Lang., (2022)Characterisation of voice quality of Parkinson's disease using differential phonological posterior features.M. Cernak, J. Orozco-Arroyave, F. Rudzicz, H. Christensen, J. Vásquez-Correa, and E. Nöth. Comput. Speech Lang., (2017)Multimodal I-vectors to Detect and Evaluate Parkinson's Disease.N. García, J. Vásquez-Correa, J. Orozco-Arroyave, and E. Nöth. INTERSPEECH, page 2349-2353. ISCA, (2018)Deep Learning for Orca Call Type Identification - A Fully Unsupervised Approach.C. Bergler, M. Schmitt, R. Cheng, A. Maier, V. Barth, and E. Nöth. INTERSPEECH, page 3357-3361. ISCA, (2019)A language-independent feature set for the automatic evaluation of prosody.A. Maier, F. Hönig, V. Zeißler, A. Batliner, E. Körner, N. Yamanaka, P. Ackermann, and E. Nöth. INTERSPEECH, page 600-603. ISCA, (2009)Representation Learning Strategies to Model Pathological Speech: Effect of Multiple Spectral Resolutions.G. Miller, J. Vásquez-Correa, J. Orozco-Arroyave, and E. Nöth. CoRR, (2022)Comparison of User Models Based on GMM-UBM and I-Vectors for Speech, Handwriting, and Gait Assessment of Parkinson's Disease Patients.J. Vásquez-Correa, T. Bocklet, J. Orozco-Arroyave, and E. Nöth. ICASSP, page 6544-6548. IEEE, (2020)

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Disambiguation of "Nöth, Elmar"

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Transfer Learning to Detect Parkinson's Disease from Speech In Different Languages Using Convolutional Neural Networks with Layer Freezing.

Please choose a person to relate this publication to

Elmar Nöth

Elmar Nöth

Elmar Nöth

Ludwig Nöth

Stefan Nöth

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Nöth, Elmar"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Transfer Learning to Detect Parkinson's Disease from Speech In Different Languages Using Convolutional Neural Networks with Layer Freezing.

Please choose a person to relate this publication to

Elmar Nöth

Elmar Nöth

Elmar Nöth

Ludwig Nöth

Stefan Nöth

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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Transfer Learning to Detect Parkinson's Disease from Speech In Different Languages Using Convolutional Neural Networks with Layer Freezing.