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 …

Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is
based on the social value of Generation Z that online and offline selves are not different …

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 …

How to leverage dynamic contextual information in end-to-end speech recognition has
remained an active research area. Previous solutions to this problem were either designed …

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 …

End-to-end speech recognition models trained using joint Connectionist Temporal
Classification (CTC)-Attention loss have gained popularity recently. In these models, a non …

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 …

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 …

Contextual biasing is an important and challenging task for end-to-end automatic speech
recognition (ASR) systems, which aims to achieve better recognition performance by biasing …