47 citations · 98 across the 18 of their papers we have counts for
19 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…
Classifying Dementia in the Presence of Depression: A Cross-Corpus Study
Franziska Braun, Sebastian P. Bayerl, Paula A. Pérez-Toro +6
Automated dementia screening enables early detection and intervention, reducing costs to healthcare systems and increasing quality of life for those affected. Depression has shared…
A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem
Sebastian P. Bayerl, Dominik Wagner, Ilja Baumann +4
Most stuttering detection and classification research has viewed stuttering as a multi-class classification problem or a binary detection task for each dysfluency type; however, th…
Generative Models for Improved Naturalness, Intelligibility, and Voicing of Whispered Speech
Dominik Wagner, Sebastian P. Bayerl, Hector A. Cordourier Maruri +1
This work adapts two recent architectures of generative models and evaluates their effectiveness for the conversion of whispered speech to normal speech. We incorporate the normal…
Speaker Adaptation for End-To-End Speech Recognition Systems in Noisy Environments
Dominik Wagner, Ilja Baumann, Sebastian P. Bayerl +2
We analyze the impact of speaker adaptation in end-to-end automatic speech recognition models based on transformers and wav2vec 2.0 under different noise conditions. By including s…
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…
An Acoustical Machine Learning Approach to Determine Abrasive Belt Wear of Wide Belt Sanders
Maximilian Bundscherer, Thomas H. Schmitt, Sebastian Bayerl +2
This paper describes a machine learning approach to determine the abrasive belt wear of wide belt sanders used in industrial processes based on acoustic data, regardless of the san…
Influence of Utterance and Speaker Characteristics on the Classification of Children with Cleft Lip and Palate
Ilja Baumann, Dominik Wagner, Franziska Braun +4
Recent findings show that pre-trained wav2vec 2.0 models are reliable feature extractors for various speaker characteristics classification tasks. We show that latent representatio…
Multi-class Detection of Pathological Speech with Latent Features: How does it perform on unseen data?
Dominik Wagner, Ilja Baumann, Franziska Braun +4
The detection of pathologies from speech features is usually defined as a binary classification task with one class representing a specific pathology and the other class representi…
Nonwords Pronunciation Classification in Language Development Tests for Preschool Children
Ilja Baumann, Dominik Wagner, Sebastian Bayerl +1
This work aims to automatically evaluate whether the language development of children is age-appropriate. Validated speech and language tests are used for this purpose to test the…
Going Beyond the Cookie Theft Picture Test: Detecting Cognitive Impairments using Acoustic Features
Franziska Braun, Andreas Erzigkeit, Hartmut Lehfeld +3
Standardized tests play a crucial role in the detection of cognitive impairment. Previous work demonstrated that automatic detection of cognitive impairment is possible using audio…
The Influence of Dataset Partitioning on Dysfluency Detection Systems
Sebastian P. Bayerl, Dominik Wagner, Elmar Nöth +2
This paper empirically investigates the influence of different data splits and splitting strategies on the performance of dysfluency detection systems. For this, we perform experim…
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…
Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0
Sebastian P. Bayerl, Dominik Wagner, Elmar Nöth +1
Stuttering is a varied speech disorder that harms an individual's communication ability. Persons who stutter (PWS) often use speech therapy to cope with their condition. Improving…
KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stuttering
Sebastian P. Bayerl, Alexander Wolff von Gudenberg, Florian Hönig +2
Stuttering is a complex speech disorder that negatively affects an individual's ability to communicate effectively. Persons who stutter (PWS) often suffer considerably under the co…