Name: Animal Speech Group Colloquium
Start: 2023-02-10
End: 2023-02-10
Location: Virtual

Apart from automatic feature extraction and subsequent speech recognition, our chair deals with the following topics: spoken dialogue systems, recognition and processing of unknown, so-called out-of-vocabulary words, automatic analysis and classification of prosodic phenomena such as accent and phrase boundaries. Another core topic is the automatic recognition of emotion-related, affective user states based on acoustic and linguistic features; moreover, we use multi-modal information for this task, including an analysis of facial expressions, gestures, and physiological parameters. Another topic is the multi-modal recognition of the user's focus of attention in human-machine-interaction. Finally, we work on the analysis of pathologic speech such as speech from children with cleft lip and palate or patients after laryngectomy (removal of the larynx after cancer).

Projects

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 …

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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…

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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.

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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.

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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…

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