47 citations · 143 across the 42 of their papers we have counts for
18 papers · 1 filter
The Spoken Wikipedia Presentation Corpus
Thomas Ranzenberger, Steffen Freisinger, Tobias Bocklet +1
We present the Spoken Wikipedia Presentation Corpus, an extension of the Spoken Wikipedia Corpora featuring LLM-generated slide decks for multimodal ASR. Slides are created from LL…
The PARLO Dementia Corpus: A German Multi-Center Resource for Alzheimer's Disease
Franziska Braun, Christopher Witzl, Florian Hönig +3
Early and accessible detection of Alzheimer's disease (AD) remains a major challenge, as current diagnostic methods often rely on costly and invasive biomarkers. Speech and languag…
Bias and Fairness in Self-Supervised Acoustic Representations for Cognitive Impairment Detection
Kashaf Gulzar, Korbinian Riedhammer, Elmar Nöth +2
Speech-based detection of cognitive impairment (CI) offers a promising non-invasive approach for early diagnosis, yet performance disparities across demographic and clinical subgro…
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…
Pitfalls and Limits in Automatic Dementia Assessment
Franziska Braun, Christopher Witzl, Andreas Erzigkeit +4
Current work on speech-based dementia assessment focuses on either feature extraction to predict assessment scales, or on the automation of existing test procedures. Most research…
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…
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…
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…
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…
Toward Zero Oracle Word Error Rate on the Switchboard Benchmark
Arlo Faria, Adam Janin, Korbinian Riedhammer +1
The "Switchboard benchmark" is a very well-known test set in automatic speech recognition (ASR) research, establishing record-setting performance for systems that claim human-level…
Automated Evaluation of Standardized Dementia Screening Tests
Franziska Braun, Markus Förstel, Bastian Oppermann +4
For dementia screening and monitoring, standardized tests play a key role in clinical routine since they aim at minimizing subjectivity by measuring performance on a variety of cog…
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…
Detecting Vocal Fatigue with Neural Embeddings
Sebastian P. Bayerl, Dominik Wagner, Ilja Baumann +2
Vocal fatigue refers to the feeling of tiredness and weakness of voice due to extended utilization. This paper investigates the effectiveness of neural embeddings for the detection…
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…
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…