Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

Personal rare word recognition in end-to-end Automatic Speech Recognition (E2E ASR)
models is a challenge due to the lack of training data. A standard way to address this issue …

Nowadays, most methods for end-to-end contextual speech recognition bias the recognition
process towards contextual knowledge. Since all-neural contextual biasing methods rely on …

Foundation models (FMs), that are trained on broad data at scale and are adaptable to a
wide range of downstream tasks, have brought large interest in the research community …

We present a data and cost efficient way of incorporating the speech modality into a large
language model (LLM). The resulting multi-modal LLM is a COntextual Speech Model with …

In recent years, Large Language Models (LLMs) have garnered significant attention from the
research community due to their exceptional performance and generalization capabilities. In …

Contextual knowledge is important for real-world automatic speech recognition (ASR)
applications. In this paper, a novel tree-constrained pointer generator (TCPGen) component …

Contextual information plays a crucial role in speech recognition technologies and
incorporating it into the end-to-end speech recognition models has drawn immense interest …

Automatic speech recognition (ASR) is increasingly being used in specialized domains such
as medical ASR and news transcription. Owing to the lack of high quality annotated speech …

End-to-end automatic speech recognition (ASR) and large language models, such as
Whisper and GPT-2, have recently been scaled to use vast amounts of training data. Despite …