34 citations · 34 across the 2 of their papers we have counts for
5 papers
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…
A Comparison of Hybrid and End-to-End Models for Syllable Recognition
Sebastian P. Bayerl, Korbinian Riedhammer
This paper presents a comparison of a traditional hybrid speech recognition system (kaldi using WFST and TDNN with lattice-free MMI) and a lexicon-free end-to-end (TensorFlow imple…
Timage -- A Robust Time Series Classification Pipeline
Marc Wenninger, Sebastian P. Bayerl, Jochen Schmidt +1
Time series are series of values ordered by time. This kind of data can be found in many real world settings. Classifying time series is a difficult task and an active area of rese…