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.

该研究通过智能家居自动化指令在乌尔都语中考察了自动语音识别（ASR）鲁棒的自然语言理解（NLU）模型从受控实验条件到实际应用的可迁移性，并评估了模型在不同噪声环境、语言变化和 ASR 错误场景下的性能。利用乌尔都 BERT 模型，研究采用了系统的方法论，包括实际数据收集、交叉验证、迁移学习、噪声变化研究和领域适应。评估指标涵盖特定任务的准确性、延迟、用户满意度和对 ASR 错误的鲁棒性。研究结果揭示了 ASR 鲁棒的 NLU 模型在超越受控环境方面所面临的挑战和适应性。