Speech Processing and Understanding
Projects
TAPAS: Training Network on Automatic Processing of PAthological Speech
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.
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.
DysarTrain: Development of a digital therapy tool as an exercise supplement for speech disorders and facial paralysis
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 …
Modelling the progression of neurological diseases
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…
DeepAL: Deep Learning Applied to Animal Linguistics
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.
Deep Learning based Noise Reduction for Hearing Aids
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…
Participating Scientists
Prof. Dr.-Ing. Elmar Nöth
- Phone number: +49 9131 85-27888
- Email: elmar.noeth@fau.de
- Website: https://lme.tf.fau.de/person/noeth/
Philipp Klumpp, M. Sc.
- Phone number: +4991318527137
- Email: philipp.klumpp@fau.de
Colloquium time table
Fatma Mami: “Understanding Odor Descriptors through Advanced NLP Models and Semantic Scores” (MT Intro talk)
MT final talk: Fine-tune large language models for radiation oncology
Large Language Model for X-ray Transcriptions and and Diagnosis Support
Publications
Phonation and articulation analyses in laryngeal pathologies, cleft lip and palate, and parkinson’s disease
Springer Verlag, 2017
ISBN: 9783319597720
DOI: 10.1007/978-3-319-59773-7_43
BibTeX: Download
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NeuroSpeech: An open-source software for Parkinson's speech analysis
In: Digital Signal Processing 77 (2018), p. 207-221
ISSN: 1051-2004
DOI: 10.1016/j.dsp.2017.07.004
BibTeX: Download
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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
BibTeX: Download
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Next Events:
Kashaf Gulzar: “Audio and Linguistic Embeddings for the Classification of Alzheimer’s Disease and Depression: A Bias Analysis and Machine Learning Investigation” (MT Final Talk)
Fatma Mami: “Understanding Odor Descriptors through Advanced NLP Models and Semantic Scores” (MT Intro talk)
MT final talk: Fine-tune large language models for radiation oncology
Large Language Model for X-ray Transcriptions and and Diagnosis Support
Contact
Paula Andrea Pérez-Toro, M. Sc.
91058 Erlangen
- Phone number: +49 9131 85-27137
- Email: paula.andrea.perez@fau.de
- Website: https://lme.tf.fau.de/person/perez