Voice Anonymization
Protecting Identity Through Voice Transformation [Archived]
Research
In this project, we develop a system for voice anonymization using a voice conversion (VC) approach, in which we convert the vocal identity of an utterance to sound like another person without changing the linguistic or prosodic content. Using a state-of-the-art deep neural network VC model, we are able to transform any speech utterance to sound like any target speaker given a sample of the target speaker’s speech. We further explore how listening to speech anonymized in this way affects peoples’ perception of the content that is conveyed, both from the point of view of the listener and the original speaker.
Collaborators
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Anthology
An AI interface that turns raw conversation audio into interactive maps
Research
Dialogue to Decision
An LLM-Powered Framework for Analyzing Collective Idea Evolution and Voting Dynamics in Deliberative Assemblies
Research
Voice to Vision
Supporting decision makers in effective and efficient constituency-informed, AI-supported decision-making. Communicating how constituen...
Research
DisCourse
A curated social experience that transforms dinner between strangers into an opportunity to reimagine how we listen, speak, and share
Research
FRONTLINE Social Dialogue Network
Archived
Over 40 young people participated in a social listening experiment exploring how personal experiences build bridges to better understanding current events and creating mo
Pilots & Programs
realtalk@Boston
A new civic infrastructure in Boston grounded in dialogue as a way to building “civic muscle” of democracy
Pilots & Programs
