8 citations · 9 across the 14 of their papers we have counts for
15 papers
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…
Adding Robust Code-Switching Capabilities to High Performance Multilingual ASR
Enes Yavuz Ugan, Alexander Waibel
Code-switching (CSW) remains challenging for large multi-lingual ASR systems in real-world deployment. While fine-tuning on synthetic CSW data is possible, it generally degrades st…
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…
MUSCAT: MUltilingual, SCientific ConversATion Benchmark
Supriti Sinhamahapatra, Thai-Binh Nguyen, Yiğit Oğuz +3
The goal of multilingual speech technology is to facilitate seamless communication between individuals speaking different languages, creating the experience as though everyone were…
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…
Adapting Language Balance in Code-Switching Speech
Enes Yavuz Ugan, Ngoc-Quan Pham, Alexander Waibel
Despite achieving impressive results on standard benchmarks, large foundational models still struggle against code-switching test cases. When data scarcity cannot be used as the us…
Bayesian Low-Rank Factorization for Robust Model Adaptation
Enes Yavuz Ugan, Ngoc-Quan Pham, Alexander Waibel
Large speech foundation models achieve strong performance across many domains, but they often require adaptation to handle local needs such as code-switching, where speakers mix la…
Weight Factorization and Centralization for Continual Learning in Speech Recognition
Enes Yavuz Ugan, Ngoc-Quan Pham, Alexander Waibel
Modern neural network based speech recognition models are required to continually absorb new data without re-training the whole system, especially in downstream applications using…
KIT's Low-resource Speech Translation Systems for IWSLT2025: System Enhancement with Synthetic Data and Model Regularization
Zhaolin Li, Yining Liu, Danni Liu +6
This paper presents KIT's submissions to the IWSLT 2025 low-resource track. We develop both cascaded systems, consisting of Automatic Speech Recognition (ASR) and Machine Translati…
PIER: A Novel Metric for Evaluating What Matters in Code-Switching
Enes Yavuz Ugan, Ngoc-Quan Pham, Leonard Bärmann +1
Code-switching, the alternation of languages within a single discourse, presents a significant challenge for Automatic Speech Recognition. Despite the unique nature of the task, pe…
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…
End-to-End Evaluation for Low-Latency Simultaneous Speech Translation
Christian Huber, Tu Anh Dinh, Carlos Mullov +10
The challenge of low-latency speech translation has recently draw significant interest in the research community as shown by several publications and shared tasks. Therefore, it is…
KIT's Multilingual Speech Translation System for IWSLT 2023
Danni Liu, Thai Binh Nguyen, Sai Koneru +7
Many existing speech translation benchmarks focus on native-English speech in high-quality recording conditions, which often do not match the conditions in real-life use-cases. In…
Code-Switching without Switching: Language Agnostic End-to-End Speech Translation
Christian Huber, Enes Yavuz Ugan, Alexander Waibel
We propose a) a Language Agnostic end-to-end Speech Translation model (LAST), and b) a data augmentation strategy to increase code-switching (CS) performance. With increasing globa…
Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition
Enes Yavuz Ugan, Christian Huber, Juan Hussain +1
Code-Switching (CS) is referred to the phenomenon of alternately using words and phrases from different languages. While today's neural end-to-end (E2E) models deliver state-of-the…