Spoken Language Understanding (SLU) enables systems to interpret user speech by identifying intents and extracting key information. It plays a critical role in voice assistants and dialogue systems, allowing them to respond appropriately. SLU typically combines ASR for converting speech to text with NLP for intent recognition and entity extraction. Deep learning models, including transformers, are often used for SLU tasks to improve accuracy and performance. SLU must handle colloquial language, ambiguous phrases, and noisy inputs. Context management is essential for multi-turn interactions, where meaning can depend on prior exchanges. Applications include smart home devices, automotive assistants, and automated customer service. SLU offers natural, intuitive ways for users to interact with systems. Future research aims to build multilingual SLU models and end-to-end systems that process speech directly into intents, bypassing intermediate steps.

TODO: please update test set description

TODO: please update test set description