Speech de-identification data augmentation leveraging large language model
This work addresses the challenge of limited real-world speech data in speech de-identification, the process of removing Personally Identifiable Information (PII). We formulate speech de-identification as a named entity recognition (NER) task specifically for spoken English. To overcome data scarcity and enhance NER performance, we propose a data augmentation approach. This approach leverages a large language model to generate synthetic speech style text data enriched with diverse PII entities. The generated data undergoes an iterative process using a customized NER model for semi-automatic PII annotation. Our analysis demonstrates the effectiveness of this data augmentation strategy in significantly improving NER performance on spoken language text. Furthermore, to gain deeper insights into the specific errors made during NER, we employ performance analysis using alternative evaluation metrics.
Funding
Acrf tier 1
History
Journal/Conference/Book title
2024 International Conference on Asian Language Processing (IALP)Publication date
2024-08-04Version
- Pre-print
Rights statement
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Corresponding author
tong.rong@singaporetech.edu.sgProject ID
- 15875 Automatic speech de-identification on Singapore English speech
- 16081 Multimodal visual acuity testing with speech and touch panel