  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.
