@inproceedings{RajasekharLeschanowskyPeters, author = {Rajasekhar, Anjana and Leschanowsky, Anna and Peters, Nils}, title = {Towards Speech Privacy Assessment for Voice Assistants: Exploring Subjective and Objective Measures for Babble Noise}, series = {Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. M{\"a}rz 2024}, booktitle = {Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. M{\"a}rz 2024}, editor = {Baumann, Timo}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-95908-325-6}, doi = {10.35096/othr/pub-7088}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-70885}, pages = {116 -- 123}, abstract = {The growing prevalence of voice assistants has sparked privacy concerns with respect to content privacy and potential human-based attacks such as eavesdropping which make users feel uncomfortable utilizing them in public. To address these challenges, understanding human privacy perceptions in acoustic environments becomes paramount. This understanding can empower voice assistants to accurately quantify privacy perceptions, adapt conversational patterns, and ultimately enhance human-machine interaction. This study draws inspiration from human-tohuman interactions and previous research on acoustic privacy, to quantify privacy perceptions in environments characterized by babble noise. The primary objective is a comprehensive evaluation of both objective and subjective measures to quantitatively capture privacy perceptions in acoustic environments.}, language = {en} }