Evaluation of Speaker Anonymization on Emotional Speech

International audienceSpeech data carries a range of personal information, such as the speaker's identity and emotional state. These attributes can be used for malicious purposes. With the development of virtual assistants, a new generation of privacy threats has emerged. Current studies have addressed the topic of preserving speech privacy. One of them, the VoicePrivacy initiative aims to promote the development of privacy preservation tools for speech technology. The task selected for the VoicePrivacy 2020 Challenge (VPC) is about speaker anonymization. The goal is to hide the source speaker's identity while preserving the linguistic information. The baseline of the VPC makes use of a voice conversion. This paper studies the impact of the speaker anonymization baseline system of the VPC on emotional information present in speech utterances. Evaluation is performed following the VPC rules regarding the attackers' knowledge about the anonymization system. Our results show that the VPC baseline system does not suppress speakers' emotions against informed attackers. When comparing anonymized speech to original speech, the emotion recognition performance is degraded by 15% relative to IEMOCAP data, similar to the degradation observed for automatic speech recognition used to evaluate the preservation of the linguistic information

Analyse de l'anonymisation du locuteur sur de la parole émotionnelle

International audienceSpeech data carries personal information, such as the speaker’s identity and emotional state, which can be used for malicious purposes. Among the studies that have addressed the topic of speech privacy, the VoicePrivacy initiative aims to promote the development of privacy tools for speechtechnologies. The goal of the VoicePrivacy Challenge 2020 (VPC) was to hide the source speaker’s identity while preserving the linguistic information. This paper studies the impact of the VPC speaker anonymization system on the emotional information of speech. Modifications to the system were also added in an attempt to mask the emotions while limiting signal degradation. Our results show that VPC baseline slightly masks the speakers’ emotions, but some modifications can mask the emotions more effectively while maintaining good intelligibility.Les données vocales contiennent des informations personnelles, telles que l'identité du locuteur ou son état émotionnel, pouvant être utilisées à des fins malveillantes. Parmi les études ayant abordé le sujet de la préservation de la confidentialité de la parole, l'initiative VoicePrivacy vise à promouvoir le développement d'outils de préservation de la vie privée pour les technologies vocales. L'objectif du VoicePrivacy Challenge 2020 (VPC) était de cacher l'identité du locuteur source tout en préservant les informations linguistiques. Cet article étudie l'impact du système d'anonymisation du locuteur du VPC sur les informations émotionnelles de la parole. Des modifications du système ont aussi été ajoutées pour essayer de masquer les émotions tout en limitant la dégradation du signal. Nos résultats montrent que la baseline du VPC masque légèrement les émotions des locuteurs mais certaines modifications permettent de masquer plus efficacement les émotions tout en conservant une bonne intelligibilité

The VoicePrivacy 2022 Challenge Evaluation Plan

For new participants - Executive summary: (1) The task is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content, paralinguistic attributes, intelligibility and naturalness. (2) Training, development and evaluation datasets are provided in addition to 3 different baseline anonymization systems, evaluation scripts, and metrics. Participants apply their developed anonymization systems, run evaluation scripts and submit objective evaluation results and anonymized speech data to the organizers. (3) Results will be presented at a workshop held in conjunction with INTERSPEECH 2022 to which all participants are invited to present their challenge systems and to submit additional workshop papers. For readers familiar with the VoicePrivacy Challenge - Changes w.r.t. 2020: (1) A stronger, semi-informed attack model in the form of an automatic speaker verification (ASV) system trained on anonymized (per-utterance) speech data. (2) Complementary metrics comprising the equal error rate (EER) as a privacy metric, the word error rate (WER) as a primary utility metric, and the pitch correlation and gain of voice distinctiveness as secondary utility metrics. (3) A new ranking policy based upon a set of minimum target privacy requirements.Comment: the file is unchanged; minor correction in metadat