Dhingra, P.Agrawal, S.Veerappan, C.S.Chng, E.S.Tong, R.2026-02-0620242024 11th International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2024, 2024, Vol., , p. -https://doi.org/10.1109/ICAICTA63815.2024.10762997https://idr.nitk.ac.in/handle/123456789/28809This 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. © 2024 IEEE.Data augmentationde-identificationnamed entity recognitionspeech recognition