SpeeDF - A Speech De-Identification Framework
Year: 2024
Type: conference-paper
Abstract: This paper proposes SpeeDF, a novel three-step framework for anonymizing speech data, particularly focusing on Singaporean English (Singlish). SpeeDF tackles the challenge of protecting less-studied Personally Identifiable Information (PII) like NRIC and passport numbers, which often go overlooked by traditional de-identification methods. Unlike approaches focused solely on entity extraction, SpeeDF leverages a combination of automatic speech recognition (ASR), named entity recognition (NER), and information anonymization. This comprehensive approach ensures thorough PII redaction while preserving the naturalness and usability of the anonymized speech data for research and various downstream applications.
Authors: Chandra Sekar Veerappan, Priyanshu Dhingra, Daniel Zhengkui Wang, Rong Tong
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4408258559