This paper considers contrastive training for cross-modal 0-shot transfer wherein a pre-
trained model in one modality is used for representation learning in another domain using …

Spoken language understanding (SLU) tasks have been studied for many decades in the
speech research community, but have not received as much attention as lower-level tasks …

Abstract Spoken Language Understanding (SLU) systems commonly use cascading
structures. However, these systems are prone to error propagation, information loss, high …

Recent studies have demonstrated promising outcomes by employing large language
models with multi-tasking capabilities. They utilize prompts to guide the model's behavior …

Conformer, a convolution-augmented Transformer variant, has become the de facto encoder
architecture for speech processing due to its superior performance in various tasks …

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 …

We propose a novel deliberation-based approach to end-to-end (E2E) spoken language
understanding (SLU), where a streaming automatic speech recognition (ASR) model …

Training a high performance end-to-end speech (E2E) processing model requires an
enormous amount of labeled speech data, especially in the era of data-centric artificial …

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 …

Although frame-based models, such as CTC and transducers, have an affinity for streaming
automatic speech recognition, their decoding uses no future knowledge, which could lead to …