超越受控环境：评估ASR鲁棒NLU模型向现实世界应用的可迁移性
Transcending Controlled Environments Assessing the Transferability of ASRRobust NLU Models to Real-World Applications
AI总结：
本文研究乌尔都语智能家居场景下ASR鲁棒NLU模型从受控环境到真实应用的迁移能力，基于UrduBERT结合迁移学习与领域自适应等方法评估其鲁棒性和适应性。
摘要：
本研究探讨了自动语音识别（ASR）鲁棒的语音自然语言理解（NLU）模型从受控实验条件迁移到实际现实世界应用的可迁移性。研究聚焦于乌尔都语智能家居自动化指令，评估模型在不同噪声分布、语言变异和ASR错误场景下的性能。研究利用UrduBERT模型，采用系统化方法，包括现实世界数据收集、交叉验证、迁移学习、噪声变异研究和领域自适应。评估指标涵盖任务特定准确率、延迟、用户满意度以及对ASR错误的鲁棒性。研究结果为ASR鲁棒NLU模型在超越受控环境时所面临的挑战和适应性提供了见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
