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.

翻译：本研究探讨了自动语音识别（ASR）鲁棒型自然语言理解（NLU）模型从受控实验条件向实际应用场景的迁移性问题。研究以乌尔都语的智能家居控制指令为焦点，评估模型在不同噪声分布、语言变异及ASR错误场景下的性能表现。依托UrduBERT模型，研究采用包含真实数据采集、交叉验证、迁移学习、噪声变化研究及领域自适应在内的系统方法论。评估指标涵盖任务级精度、响应延迟、用户满意度及ASR错误的鲁棒性。研究结果为ASR鲁棒型NLU模型在超越受控环境时的挑战与适应性问题提供了洞见。