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Long-term Conversation Analysis: Exploring Utility and Privacy
- Publication Year :
- 2023
-
Abstract
- The analysis of conversations recorded in everyday life requires privacy protection. In this contribution, we explore a privacy-preserving feature extraction method based on input feature dimension reduction, spectral smoothing and the low-cost speaker anonymization technique based on McAdams coefficient. We assess the utility of the feature extraction methods with a voice activity detection and a speaker diarization system, while privacy protection is determined with a speech recognition and a speaker verification model. We show that the combination of McAdams coefficient and spectral smoothing maintains the utility while improving privacy.<br />Comment: Submitted to ITG Conference on Speech Communication, 2023
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2306.16071
- Document Type :
- Working Paper