Long-term Conversation Analysis: Exploring Utility and Privacy

Author:

Francesco Nespoli, Jule Pohlhausen, Patrick A. Naylor, Joerg Bitzer

Keyword:

Electrical Engineering and Systems Science, Audio and Speech Processing, Audio and Speech Processing (eess.AS), Computation and Language (cs.CL), Sound (cs.SD)

journal:

date:

2023-06-27 16:00:00

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.

PDF: Long-term Conversation Analysis: Exploring Utility and Privacy.pdf