8 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…
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…
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…
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…
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…