34 citations · 38 across the 5 of their papers we have counts for
8 papers
Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning
Philipp Seeberger, Korbinian Riedhammer
Social media has become an important information source for crisis management and provides quick access to ongoing developments and critical information. However, classification mo…
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem
Sebastian P. Bayerl, Dominik Wagner, Florian Hönig +3
Specially adapted speech recognition models are necessary to handle stuttered speech. For these to be used in a targeted manner, stuttered speech must be reliably detected. Recent…
The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes
Björn W. Schuller, Anton Batliner, Shahin Amiriparian +12
The ACM Multimedia 2022 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the…
STAN: A stuttering therapy analysis helper
Sebastian P. Bayerl, Marc Wenninger, Jochen Schmidt +2
Stuttering is a complex speech disorder identified by repeti-tions, prolongations of sounds, syllables or words and blockswhile speaking. Specific stuttering behaviour differs stro…
Offline Model Guard: Secure and Private ML on Mobile Devices
Sebastian P. Bayerl, Tommaso Frassetto, Patrick Jauernig +5
Performing machine learning tasks in mobile applications yields a challenging conflict of interest: highly sensitive client information (e.g., speech data) should remain private wh…
Towards Automated Assessment of Stuttering and Stuttering Therapy
Sebastian P. Bayerl, Florian Hönig, Joelle Reister +1
Stuttering is a complex speech disorder that can be identified by repetitions, prolongations of sounds, syllables or words, and blocks while speaking. Severity assessment is usuall…