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Found 11 papers, 4 papers with code
The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation
The VoicePrivacy Challenge promotes the development of voice anonymisation solutions for speech technology.
Open-Source Conversational AI with SpeechBrain 1.0
This paper presents SpeechBrain 1. 0, a significant milestone in the evolution of the toolkit, which now has over 200 recipes for speech, audio, and language processing tasks, and more than 100 models available on Hugging Face.
The VoicePrivacy 2024 Challenge Evaluation Plan
The task of the challenge is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content and emotional states.
Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques
The growing use of voice user interfaces has led to a surge in the collection and storage of speech data.
Evaluation of Speaker Anonymization on Emotional Speech
This paper studies the impact of the speaker anonymization baseline system of the VPC on emotional information present in speech utterances.
Are disentangled representations all you need to build speaker anonymization systems?
We propose enhancing the disentanglement by removing speaker information from the acoustic model using vector quantization.
The VoicePrivacy 2022 Challenge Evaluation Plan
Participants apply their developed anonymization systems, run evaluation scripts and submit objective evaluation results and anonymized speech data to the organizers.
Privacy-Preserving Speech Representation Learning using Vector Quantization
With the popularity of virtual assistants (e. g., Siri, Alexa), the use of speech recognition is now becoming more and more widespread. However, speech signals contain a lot of sensitive information, such as the speaker's identity, which raises privacy concerns. The presented experiments show that the representations extracted by the deep layers of speech recognition networks contain speaker information. This paper aims to produce an anonymous representation while preserving speech recognition performance. To this end, we propose to use vector quantization to constrain the representation space and induce the network to suppress the speaker identity. The choice of the quantization dictionary size allows to configure the trade-off between utility (speech recognition) and privacy (speaker identity concealment).
On the invertibility of a voice privacy system using embedding alignement
This paper explores various attack scenarios on a voice anonymization system using embeddings alignment techniques.
Evaluating X-vector-based Speaker Anonymization under White-box Assessment
In the scenario of the Voice Privacy challenge, anonymization is achieved by converting all utterances from a source speaker to match the same target identity; this identity being randomly selected.
A Study of F0 Modification for X-Vector Based Speech Pseudonymization Across Gender
Speech pseudonymization aims at altering a speech signal to map the identifiable personal characteristics of a given speaker to another identity.
