Tomas Arias Vergara, M. Sc.
I received the B.S. degree in Electronics Engineering from University of Antioquia (Medellin, Colombia) in 2014, and the Master of Science degree at the same institution in 2017. Currently I am a PhD student at University of Antioquia (Colombia) and at Friedrich Alexander University Erlangen-Nuremberg (Germany) in a joint degree program. Additionally, I’m a guest researcher in the Department of Otorhinolaryngology, Head and Neck Surgery from the Ludwig-Maximilians-Universität in Munich (Germany). Also, I love Schnitzel.
- Since May/2018:
Researcher at Ludwig-Maximilians University, Munich, Germany - Since May/2018:
Marie Curie Research Fellow at Training Network on Automatic Processing of PAthological Speech – TAPAS - Since May/2018:
PhD Candidate at Friedrich-Alexander University, Erlangen-Nuernberg, Germany - Since Jan/2017:
PhD Candidate at University of Antioquia, Medellin, Colombia
2017
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Training Network on Automatic Processing of PAthological Speech
(Third Party Funds Group – Overall project)
Term: November 1, 2017 - October 31, 2021
Funding source: Innovative Training Networks (ITN)
URL: https://www.tapas-etn-eu.org/There are an increasing number of people across Europe with debilitating speech pathologies (e.g., due to stroke, Parkinson's, etc). These groups face communication problems that can lead to social exclusion. They are now being further marginalised by a new wave of speech technology that is increasingly woven into everyday life but which is not robust to atypical speech. TAPAS is a Horizon 2020 Marie Skłodowska-Curie Actions Innovative Training Network European Training Network (MSCA-ITN-ETN) project that aims to transform the well being of these people.
The TAPAS work programme targets three key research problems:
(a) Detection: We will develop speech processing techniques for early detection of conditions that impact on speech production. The outcomes will be cheap and non-invasive diagnostic tools that provide early warning of the onset of progressive conditions such as Alzheimer's and Parkinson's.
(b) Therapy: We will use newly-emerging speech processing techniques to produce automated speech therapy tools. These tools will make therapy more accessible and more individually targeted. Better therapy can increase the chances of recovering intelligible speech after traumatic events such a stroke or oral surgery.
(c) Assisted Living: We will re-design current speech technology so that it works well for people with speech impairments and also helps in making informed clinical choices. People with speech impairments often have other co-occurring conditions making them reliant on carers. Speech-driven tools for assisted-living are a way to allow such people to live more independently.
TAPAS adopts an inter-disciplinary and multi-sectorial approach. The consortium includes clinical practitioners, academic researchers and industrial partners, with expertise spanning speech engineering, linguistics and clinical science. All members have expertise in some element of pathological speech. This rich network will train a new generation of 15 researchers, equipping them with the skills and resources necessary for lasting success.
2020
Journal Articles
Automatic detection of Voice Onset Time in voiceless plosives using gated recurrent units
In: Digital Signal Processing 104 (2020), Article No.: 102779
ISSN: 1051-2004
DOI: 10.1016/j.dsp.2020.102779
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Parallel Representation Learning for the Classification of Pathological Speech: Studies on Parkinson's Disease and Cleft Lip and Palate
In: Speech Communication 122 (2020), p. 56-67
ISSN: 0167-6393
DOI: 10.1016/j.specom.2020.07.005
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Nonlinear dynamics and Poincaré sections to model gait impairments in different stages of Parkinson’s disease
In: Nonlinear Dynamics (2020)
ISSN: 0924-090X
DOI: 10.1007/s11071-020-05691-7
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2019
Book Contributions
Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson’s Disease from Speech in Three Different Languages
In: Ingela Nyström, Yanio Hernández Heredia, Vladimir Milián Núñez (ed.): Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2019, p. 697-706 (Image Processing, Computer Vision, Pattern Recognition, and Graphics, Vol.11896)
ISBN: 9783030339036
DOI: 10.1007/978-3-030-33904-3_66
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Multi-channel Convolutional Neural Networks for Automatic Detection of Speech Deficits in Cochlear Implant Users
In: Ingela Nyström, Yanio Hernández Heredia, Vladimir Milián Núñez (ed.): Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2019, p. 679-687 (Image Processing, Computer Vision, Pattern Recognition, and Graphics, Vol.11896)
ISBN: 9783030339036
DOI: 10.1007/978-3-030-33904-3_64
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Conference Contributions
Speech differences between CI users with pre- and postlingual onset of deafness detected by speech processing methods on voiceless to voice transitions
90. Jahresversammlung der Deutschen Gesellschaft für Hals-Nasen-Ohren-Heilkunde, Kopf- und Hals-Chirurgie (Estrel Congress Center Berlin, May 29, 2019 - June 1, 2019)
In: Laryngo-Rhino-Otol 2019 2019
DOI: 10.1055/s-0039-168632
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Consonant-to-Vowel/Vowel-to-Consonant Transitions to Analyze the Speech of Cochlear Implant Users.
Text, Speech, and Dialogue 2019 (Ljubljana, September 11, 2019 - September 13, 2019)
In: Kamil Ekštein (ed.): Lecture Notes in Computer Science 2019
DOI: 10.1007/978-3-030-27947-9_25
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Phone-attribute posteriors to evaluate the speech of cochlear implant users
20th Annual Conference of the International Speech Communication Association: Crossroads of Speech and Language, INTERSPEECH 2019 (Graz, September 15, 2019 - September 19, 2019)
In: Gernot Kubin, Thomas Hain, Bjorn Schuller, Dina El Zarka, Petra Hodl (ed.): Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2019
DOI: 10.21437/Interspeech.2019-2144
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Multi-channel Convolutional Neural Networks for Automatic Detection of Speech Deficits in Cochlear Implant Users
Iberoamerican Congress on Pattern Recognition
In: CIARP 2019: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications 2019
DOI: 10.1007/978-3-030-33904-3_64
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2018
Journal Articles
Multimodal assessment of Parkinson's disease: a deep learning approach
In: IEEE Journal of Biomedical and Health Informatics (2018)
ISSN: 2168-2194
DOI: 10.1109/JBHI.2018.2866873
URL: https://ieeexplore.ieee.org/document/8444654
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Speaker models for monitoring Parkinson’s disease progression considering different communication channels and acoustic conditions
In: Speech Communication 101 (2018), p. 11-25
ISSN: 0167-6393
DOI: 10.1016/j.specom.2018.05.007
URL: https://www.sciencedirect.com/science/article/abs/pii/S0167639317304454
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Conference Contributions
A Multitask Learning Approach to Assess the Dysarthria Severity in Patients with Parkinson's Disease
INTERSPEECH
In: Proceedings of INTERSPEECH 2018
DOI: 10.21437/Interspeech.2018-1988
URL: https://www.isca-speech.org/archive/Interspeech_2018/abstracts/1988.html
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Unobtrusive Monitoring of Speech Impairments of Parkinson's Disease Patients Through Mobile Devices
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
DOI: 10.1109/ICASSP.2018.8462332
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2017
Authored Books
Speaker model to monitor the neurological state and the dysarthria level of patients with parkinson’s disease
Springer Verlag, 2017
ISBN: 9783319642055
DOI: 10.1007/978-3-319-64206-2_31
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Conference Contributions
Apkinson — A Mobile Monitoring Solution for Parkinson's Disease
Interspeech 2017
In: Interspeech 2017 2017
URL: https://www.researchgate.net/profile/Juan_Vasquez12/publication/319185470_Apkinson_-_A_Mobile_Monitoring_Solution_for_Parkinson's_Disease/links/59b9fecfa6fdcc68723177dc/Apkinson-A-Mobile-Monitoring-Solution-for-Parkinsons-Disease.pdf
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Parkinson’s disease progression assessment from speech using a mobile device-based application
20th International Conference on Text, Speech and Dialogue, TSD 2017
In: TSD 2017: Text, Speech, and Dialogue 2017
DOI: 10.1007/978-3-319-64206-2_42
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2016
Conference Contributions
Gender-dependent GMM-UBM for Tracking Parkinson's Disease Progression from Speech
12. ITG Fachtagung Sprachkommunikation (Paderborn)
In: Speech Communication - 12. ITG Fachtagung Sprachkommunikation, Berlin: 2016
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2016/Arias-Vergara16-GGF.pdf
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