  This research investigates the transferability of Automatic Speech
Recognition (ASR)-robust Natural Language Understanding (NLU) models from
controlled experimental conditions to practical, real-world applications.
Focused on smart home automation commands in Urdu, the study assesses model
performance under diverse noise profiles, linguistic variations, and ASR error
scenarios. Leveraging the UrduBERT model, the research employs a systematic
methodology involving real-world data collection, cross-validation, transfer
learning, noise variation studies, and domain adaptation. Evaluation metrics
encompass task-specific accuracy, latency, user satisfaction, and robustness to
ASR errors. The findings contribute insights into the challenges and
adaptability of ASR-robust NLU models in transcending controlled environments.
