2020/12/08 by Paul-Gauthier Noé, Paul-Gauthier Noé, Noé, Paul-Gauthier +10 · 4 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #FOS: Electrical engineering #Natural Language Processing Techniques #Speech Recognition and Synthesis #Topic Modeling #cs.AI #cs.CR #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2012.04454
Accepted to Interspeech 2021
openalex publication_date 2020/12/08 · arxiv created 2021/06/16 · arxiv updated 2021/06/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In speech technologies, speaker's voice representation is used in many applications such as speech recognition, voice conversion, speech synthesis and, obviously, user authentication. Modern vocal representations of the speaker are based on neural embeddings. In addition to the targeted information, these representations usually contain sensitive information about the speaker, like the age, sex, physical state, education level or ethnicity. In order to allow the user to choose which information to protect, we introduce in this paper the concept of attribute-driven privacy preservation in speaker voice representation. It allows a person to hide one or more personal aspects to a potential malicious interceptor and to the application provider. As a first solution to this concept, we propose to use an adversarial autoencoding method that disentangles in the voice representation a given speaker attribute thus allowing its concealment. We focus here on the sex attribute for an Automatic Speaker Verification (ASV) task. Experiments carried out using the VoxCeleb datasets have shown that the proposed method enables the concealment of this attribute while preserving ASV ability.