Manually annotating fine-grained slot-value labels for task-oriented dialogue (ToD) systems
is an expensive and time-consuming endeavour. This motivates research into slot-filling …

Large language models (LLMs) have demonstrated remarkable advancements in language
understanding and generation. Building on the success of text-based LLMs, recent research …

We previously proposed contextual spelling correction (CSC) to correct the output of end-to-
end (E2E) automatic speech recognition (ASR) models with contextual information such as …

There has been an increased interest in the integration of pretrained speech recognition
(ASR) and language models (LM) into the SLU framework. However, prior methods often …

Recently there have been efforts to introduce new benchmark tasks for spoken language
understanding (SLU), like semantic parsing. In this paper, we describe our proposed spoken …

End-to-end (E2E) spoken language understanding (SLU) systems that generate a semantic
parse from speech have become more promising recently. This approach uses a single …

Spoken semantic parsing (SSP) involves generating machine-comprehensible parses from
input speech. Training robust models for existing application domains represented in …

Spoken Language Understanding (SLU) is a critical speech recognition application and is
often deployed on edge devices. Consequently, on-device processing plays a significant …

Recent studies find existing self-supervised speech encoders contain primarily acoustic
rather than semantic information. As a result, pipelined supervised automatic speech …

Spoken Language Understanding (SLU) is a critical component of voice assistants; it
consists of converting speech to semantic parses for task execution. Previous works have …