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arXiv · 2306.16071

Long-term Conversation Analysis: Exploring Utility and Privacy

Francesco Nespoli·Jule Pohlhausen·Patrick A. Naylor·Joerg Bitzer
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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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Francesco Nespoli, Jule Pohlhausen, Patrick A. Naylor, Joerg Bitzer. 2023-06-28. Long-term Conversation Analysis: Exploring Utility and Privacy. https://arxiv.org/abs/2306.16071

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