Long-term Conversation Analysis: Exploring Utility and Privacy

• URL: http://arxiv.org/abs/2306.16071v1
• Date: Wed, 28 Jun 2023 10:10:57 GMT
• Title: Long-term Conversation Analysis: Exploring Utility and Privacy
• Authors: Francesco Nespoli, Jule Pohlhausen, Patrick A. Naylor, Joerg Bitzer
• Abstract summary: 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 show that the combination of McAdams coefficient and spectral smoothing maintains the utility while improving privacy.
• Score: 12.380029887841175
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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