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

Contextualized end-to-end automatic speech recognition has been an active research area,
with recent efforts focusing on the implicit learning of contextual phrases based on the final …

The attention-based deep contextual biasing method has been demonstrated to effectively
improve the recognition performance of end-to-end automatic speech recognition (ASR) …

Contextual biasing enables speech recognizers to transcribe important phrases in the
speaker's context, such as contact names, even if they are rare in, or absent from, the …

Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR)
systems towards rare entities that are relevant to the specific user or application scenarios …

In this paper, we propose a Guided Attention (GA) auxiliary training loss, which improves the
effectiveness and robustness of automatic speech recognition (ASR) contextual biasing …

Deep biasing methods and shallow fusion methods have been demonstrated to improve the
performance of end-to-end ASR effectively. However, accurate recognition often becomes …

Although contextualized automatic speech recognition (ASR) systems are commonly used to
improve the recognition of uncommon words, their effectiveness is hindered by the inherent …

Although end-to-end (E2E) automatic speech recognition (ASR) systems excel in general
tasks, they frequently struggle with accurately recognizing personal rare words. Leveraging …

Contextualized ASR models have been demonstrated to effectively improve the recognition
accuracy of uncommon phrases when a predefined phrase list is available. However, these …