Nishant Prateek — accepted AI-conference papers

2020

BOFFIN TTS: Few-Shot Speaker Adaptation by Bayesian Optimization

Henry B. Moss, Vatsal Aggarwal, Nishant Prateek, Javier González, Roberto Barra-Chicote

ICASSP 2020accepted

We present BOFFIN TTS (Bayesian Optimization For FIne-tuning Neural Text To Speech), a novel approach for few-shot speaker adaptation. Here, the task is to fine-tune a pre-trained TTS model to mimic a new speaker using a small corpus of target utterances. We demonstrate that there does not exist a o…

Cited by 59 Source ↗Scholar ↗

2020

Using Vaes and Normalizing Flows for One-Shot Text-To-Speech Synthesis of Expressive Speech

Vatsal Aggarwal, Marius Cotescu, Nishant Prateek, Jaime Lorenzo-Trueba, Roberto Barra-Chicote

ICASSP 2020accepted

We propose a Text-to-Speech method to create an unseen expressive style using one utterance of expressive speech of around one second. Specifically, we enhance the disentanglement capabilities of a state-of-the-art sequence-to-sequence based system with a Variational AutoEncoder (VAE) and a Househol…

Cited by 33 Source ↗Scholar ↗