183 citations · 767 across the 29 of their papers we have counts for
33 papers
Stochastic Pitch Prediction Improves the Diversity and Naturalness of Speech in Glow-TTS
Sewade Ogun, Vincent Colotte, Emmanuel Vincent
Flow-based generative models are widely used in text-to-speech (TTS) systems to learn the distribution of audio features (e.g., Mel-spectrograms) given the input tokens and to samp…
How to (virtually) train your speaker localizer
Prerak Srivastava, Antoine Deleforge, Archontis Politis +1
Learning-based methods have become ubiquitous in speaker localization. Existing systems rely on simulated training sets for the lack of sufficiently large, diverse and annotated re…
Can we use Common Voice to train a Multi-Speaker TTS system?
Sewade Ogun, Vincent Colotte, Emmanuel Vincent
Training of multi-speaker text-to-speech (TTS) systems relies on curated datasets based on high-quality recordings or audiobooks. Such datasets often lack speaker diversity and are…
Realistic sources, receivers and walls improve the generalisability of virtually-supervised blind acoustic parameter estimators
Prerak Srivastava, Antoine Deleforge, Emmanuel Vincent
Blind acoustic parameter estimation consists in inferring the acoustic properties of an environment from recordings of unknown sound sources. Recent works in this area have utilize…
The VoicePrivacy 2020 Challenge Evaluation Plan
Natalia Tomashenko, Brij Mohan Lal Srivastava, Xin Wang +8
The VoicePrivacy Challenge aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the…
Differentially Private Speaker Anonymization
Ali Shahin Shamsabadi, Brij Mohan Lal Srivastava, Aurélien Bellet +5
Sharing real-world speech utterances is key to the training and deployment of voice-based services. However, it also raises privacy risks as speech contains a wealth of personal da…