2 citations · 5 across the 9 of their papers we have counts for
9 papers
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…
Outlier Reduction with Gated Attention for Improved Post-training Quantization in Large Sequence-to-sequence Speech Foundation Models
Dominik Wagner, Ilja Baumann, Korbinian Riedhammer +1
This paper explores the improvement of post-training quantization (PTQ) after knowledge distillation in the Whisper speech foundation model family. We address the challenge of outl…
A Survey of Music Generation in the Context of Interaction
Ismael Agchar, Ilja Baumann, Franziska Braun +4
In recent years, machine learning, and in particular generative adversarial neural networks (GANs) and attention-based neural networks (transformers), have been successfully used t…
Multi-Query Focused Disaster Summarization via Instruction-Based Prompting
Philipp Seeberger, Korbinian Riedhammer
Automatic summarization of mass-emergency events plays a critical role in disaster management. The second edition of CrisisFACTS aims to advance disaster summarization based on mul…
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…
Combining Deep Neural Reranking and Unsupervised Extraction for Multi-Query Focused Summarization
Philipp Seeberger, Korbinian Riedhammer
The CrisisFACTS Track aims to tackle challenges such as multi-stream fact-finding in the domain of event tracking; participants' systems extract important facts from several disast…