# Transcending Controlled Environments Assessing the Transferability of ASRRobust NLU Models to Real-World Applications

arXiv: https://arxiv.org/abs/2401.09354 · HTML: https://mlsanity.com/paper/2401.09354/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 2 of 2)

- [Knowledge-Retrieval Task-Oriented Dialog Systems with Semi-Supervision](https://mlsanity.com/paper/2305.13199/) (2023)
- [Towards More Robust Natural Language Understanding](https://mlsanity.com/paper/2112.02992/) (2021)

## Abstract (arXiv)

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.