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Enhancing Speech De-Identification with LLM-Based Data Augmentation
(Institute of Electrical and Electronics Engineers Inc., 2024) Dhingra, P.; Agrawal, S.; Veerappan, C.S.; Chng, E.S.; Tong, R.
This paper addresses the challenge of data scarcity in speech de-identification by introducing a novel, fully automated data augmentation method leveraging large language models. Our approach overcomes the limitations of human annotation, enabling the creation of extensive training datasets. To enhance de-identification performance, we compare pipeline and end-to-end models. While the pipeline approach sequentially applies speech recognition and named entity recognition, the end-to-end model jointly learns these tasks. Experimental results demonstrate the effectiveness of our data augmentation strategy and the superiority of the end-to-end model in improving PII detection accuracy and robustness. Â© 2024 IEEE.