2 citations · 3 across the 16 of their papers we have counts for
14 papers · 1 filter
Speech-to-SOAP: End-to-End Summarization of Medical Dialogues: KIT@BeTraC 2026
Enes Yavuz Ugan, Fabian Retkowski, Yuka Ko +4
With the advent of Large Language Models and its instruction following capabilities a promising application is the task of summarization. Within this domain of task the extractive…
How to Recognize New Words: A Comparison Between Context Biasing Methods and Speech LLMs
Christian Huber, Alexander Waibel
Recognizing new and rare words - named entities, acronyms, domain specific special words, and other items scarce in training data - remains a key challenge for automatic speech rec…
The Role of Disfluencies in Speech Translation
Maike Züfle, Maria Teleki, Fabian Retkowski +5
Current speech translation systems, including SpeechLLMs, are trained on cleaned text and tend to strip disfluencies like filled pauses and false starts rather than translate them.…
Multilingual Long-Form Speech Instruction Following: KIT's Submission to IWSLT 2026
Enes Yavuz Ugan, Maike Züfle, Yuka Ko +5
With the advent of Large Language Models, single-task and token-based multi-task models have evolved into instruction-based systems that infer task and target language implicitly f…
Adapting Foundation ASR Models to Dysarthric Speech: A Case Study
Christian Huber, Laura Kernahan, Alexander Waibel
Automatic speech recognition (ASR) systems often perform poorly in dysarthric speech, limiting their usefulness to affected speakers in everyday communication. This paper presents…
Do What I Say: A Spoken Prompt Dataset for Instruction-Following
Maike Züfle, Sara Papi, Fabian Retkowski +5
Speech Large Language Models (SLLMs) have rapidly expanded, supporting a wide range of tasks. These models are typically evaluated using text prompts, which may not reflect real-wo…
Paragraph Segmentation Revisited: Towards a Standard Task for Structuring Speech
Fabian Retkowski, Alexander Waibel
Automatic speech transcripts are often delivered as unstructured word streams that impede readability and repurposing. We recast paragraph segmentation as the missing structuring s…
BOOM: Beyond Only One Modality KIT's Multimodal Multilingual Lecture Companion
Sai Koneru, Fabian Retkowski, Christian Huber +5
The globalization of education and rapid growth of online learning have made localizing educational content a critical challenge. Lecture materials are inherently multimodal, combi…
Context Biasing for Pronunciation-Orthography Mismatch in Automatic Speech Recognition
Christian Huber, Alexander Waibel
Neural sequence-to-sequence systems deliver state-of-the-art performance for automatic speech recognition. When using appropriate modeling units, e.g., byte-pair encoding, these sy…
The AI Co-Ethnographer: How Far Can Automation Take Qualitative Research?
Fabian Retkowski, Andreas Sudmann, Alexander Waibel
Qualitative research often involves labor-intensive processes that are difficult to scale while preserving analytical depth. This paper introduces The AI Co-Ethnographer (AICoE), a…
Zero-Shot Strategies for Length-Controllable Summarization
Fabian Retkowski, Alexander Waibel
Large language models (LLMs) struggle with precise length control, particularly in zero-shot settings. We conduct a comprehensive study evaluating LLMs' length control capabilities…
Titanic Calling: Low Bandwidth Video Conference from the Titanic Wreck
Fevziye Irem Eyiokur, Christian Huber, Thai-Binh Nguyen +5
In this paper, we report on communication experiments conducted in the summer of 2022 during a deep dive to the wreck of the Titanic. Radio transmission is not possible in deep sea…
From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions
Fabian Retkowski, Alexander Waibel
Text segmentation is a fundamental task in natural language processing, where documents are split into contiguous sections. However, prior research in this area has been constraine…
Continuously Learning New Words in Automatic Speech Recognition
Christian Huber, Alexander Waibel
Despite recent advances, Automatic Speech Recognition (ASR) systems are still far from perfect. Typical errors include acronyms, named entities, and domain-specific special words f…