8 citations · 9 across the 14 of their papers we have counts for
6 papers · 2 filters
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…