Speech Processing and Understanding
Develop speech technology that can allow unobtrusive monitoring of many kinds of neurological diseases. The state of a patient can degrade slowly between medical check-ups. We want to track the state of a patient unobtrusively without the feeling of constant supervision. At the same time the privacy of the patient has to be respected. We will concentrate on PD and thus on acoustic cues of changes. The algorithms should run on a smartphone, track acoustic changes during regular phone conversations…
Dysarthrien sind neurologisch bedingte, erworbene Störungen des Sprechens. Dabei sind vor allem die Koordination und Ausführung der Sprechbewegungen, aber auch die Mimik betroffen. Besonders häufig tritt eine Dysarthrie nach einem Schlaganfall, Schädel-Hirn-Trauma oder bei neurologischen Erkrankungen wie Parkinson auf.
Ähnlich wie in allen Sprechtherapien erfordert auch die Behandlung der Dysarthrie ein intensives Training. Anhaltende Effekte der Dysarthrie-Therapie stellen sich deshalb nur …
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
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.
Deep Learning applied to animal linguistics in particular the analysis of underwater audio recordings of marine animals (killer whales):
The project includes the automatic segmentation of killer whale signals in noise-heavy and large underwater bioacoustic archives as well as a subsequent call type identification/classification in order to derive linguistic elements/patterns. In combination with the recorded situational video footage those patterns should help to decode the killer whale language.
Reduction of unwanted environmental noises is an important feature of today’s hearing aids, which is why noise reduction is nowadays included in almost every commercially available device. The majority of these algorithms, however, is restricted to the reduction of stationary noises. Due to the large number of different background noises in daily situations, it is hard to heuristically cover the complete solution space of noise reduction schemes. Deep learning-based algorithms pose a possible so…
Apkinson: A mobile solution for multimodal assessment of patients with Parkinson's disease
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
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Word accuracy and dynamic time warping to assess intelligibility deficits in patients with Parkinsons disease
21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016
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Der mixed-initiative Ansatz als Basis für benutzerfreundliche Sprachdialogsysteme
In: Künstliche Intelligenz 19.0 (2005), p. 38-41
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Analysis and evaluation of handwriting in patients with Parkinson’s disease using kinematic, geometrical, and non-linear features
In: Computer Methods and Programs in Biomedicine 173 (2019), p. 43-52
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Characterization of voice quality of Parkinson's disease using differential phonological posterior features
In: Computer Speech and Language 46 (2017), p. 196-208
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