Nvidia Corporation

Rohan Badlani

Bryan Catanzaro

Kevin Shih

Wei Ping

Jose Rafael Valle Gomes Da Costa

Adrian Lancucki

José Rafael Valle Gomes Da Costa

João Felipe Santos

Disclosed are apparatuses, systems, and techniques that may use machine learning for implementing generative text-to-speech models. The techniques include identifying a mapping of speech characteristics (SC) on a target distribution of a latent variable using a non-linear transformation for at least a subset of the SC. Parameters of the non-linear transformation are determined using a neural network that approximates a statistics of the SC with a statistics predicted for the SC based on the identified mapping and the target distribution of the latent variable.

Normalizing flows with neural splines for high-quality speech synthesis

Generation of synthetic speech from an input text sequence may be difficult when durations of individual phonemes forming the input text sequence are unknown. A predominantly parallel process may model speech rhythm as a separate generative distribution such that phoneme duration may be sampled at inference. Additional information such as pitch or energy may also be sampled to provide improved diversity for synthetic speech generation.

Unsupervised alignment for text to speech synthesis using neural networks

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San Jose, CA, United States of America

Rohan Badlani