5 papers
Stuttering Classification and Segmentation with Attention-Based Multiple Instance Learning
Petar Sušac, Sebastian P. Bayerl, Hrvoje Džapo
Stuttering detection and classification using deep learning methods has the potential to improve the process of stuttering severity assessment. Most stuttering classification datas…
Multilingual Stutter Event Detection for English, German, and Mandarin Speech
Felix Haas, Sebastian P. Bayerl
This paper presents a multi-label stuttering detection system trained on multi-corpus, multilingual data in English, German, and Mandarin.By leveraging annotated stuttering data fr…
On the Difficulty of Token-Level Modeling of Dysfluency and Fluency Shaping Artifacts
Kashaf Gulzar, Dominik Wagner, Sebastian P. Bayerl +3
Automatic transcription of stuttered speech remains a challenge, even for modern end-to-end (E2E) automatic speech recognition (ASR) frameworks. Dysfluencies and fluency-shaping ar…
Infusing Acoustic Pause Context into Text-Based Dementia Assessment
Franziska Braun, Sebastian P. Bayerl, Florian Hönig +4
Speech pauses, alongside content and structure, offer a valuable and non-invasive biomarker for detecting dementia. This work investigates the use of pause-enriched transcripts in…
Large Language Models for Dysfluency Detection in Stuttered Speech
Dominik Wagner, Sebastian P. Bayerl, Ilja Baumann +3
Accurately detecting dysfluencies in spoken language can help to improve the performance of automatic speech and language processing components and support the development of more…