Transcending Controlled Environments Assessing the Transferability of ASRRobust NLU Models to Real-World Applications
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866916095064014848 |
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| author | Khan, Hania Khalid, Aleena Fatima Hassan, Zaryab |
| author_facet | Khan, Hania Khalid, Aleena Fatima Hassan, Zaryab |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_09354 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transcending Controlled Environments Assessing the Transferability of ASRRobust NLU Models to Real-World Applications Khan, Hania Khalid, Aleena Fatima Hassan, Zaryab Audio and Speech Processing Artificial Intelligence Sound 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. |
| title | Transcending Controlled Environments Assessing the Transferability of ASRRobust NLU Models to Real-World Applications |
| topic | Audio and Speech Processing Artificial Intelligence Sound |
| url | https://arxiv.org/abs/2401.09354 |