8 citations · 9 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
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…
Handling Numeric Expressions in Automatic Speech Recognition
Christian Huber, Alexander Waibel
This paper addresses the problem of correctly formatting numeric expressions in automatic speech recognition (ASR) transcripts. This is challenging since the expected transcript fo…
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…
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…
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…
Short-Term Word-Learning in a Dynamically Changing Environment
Christian Huber, Rishu Kumar, Ondřej Bojar +1
Neural sequence-to-sequence automatic speech recognition (ASR) systems are in principle open vocabulary systems, when using appropriate modeling units. In practice, however, they o…
Instant One-Shot Word-Learning for Context-Specific Neural Sequence-to-Sequence Speech Recognition
Christian Huber, Juan Hussain, Sebastian Stüker +1
Neural sequence-to-sequence systems deliver state-of-the-art performance for automatic speech recognition (ASR). When using appropriate modeling units, e.g., byte-pair encoded char…