Work

Long-term Conversation Analysis: Exploring Utility and Privacy

Year: 2023

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2306.16071

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

Institutions: Microsoft (United Kingdom), Microsoft Research (United Kingdom), Imperial College London, Jade University of Applied Sciences, Fraunhofer Institute for Digital Media Technology, Fraunhofer-Gesellschaft

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: European Commission

Award: G434337789

OpenAlex ID: W4382618809

Cites:

Cited by: 1

Related to: 10