End to End Spoken Language

End-to-end spoken language understanding (SLU) aims to directly translate spoken audio into semantic representations, bypassing the traditional pipeline approach of separate speech recognition and natural language understanding. Current research emphasizes improving model robustness to noisy audio and ASR errors, often employing transformer-based architectures and techniques like knowledge distillation and multi-task learning to enhance accuracy and efficiency, particularly in low-resource settings. This field is crucial for advancing human-computer interaction, enabling more natural and effective voice-controlled interfaces for applications ranging from virtual assistants to smart home devices.