US 2022/0189456 A1 · Unsupervised Learning of Disentangled Speech Content and Style Representation

Abstract

A linguistic content and speaking style disentanglement model includes a content encoder, a style encoder, and a decoder. The content encoder is configured to receive input speech as input and generate a latent representation of linguistic content for the input speech output. The content encoder is trained to disentangle speaking style information from the latent representation of linguistic content. The style encoder is configured to receive the input speech as input and generate a latent representation of speaking style for the input speech as output. The style encoder is trained to disentangle linguistic content information from the latent representation of speaking style. The decoder is configured to generate output speech based on the latent representation of linguistic content for the input speech and the latent representation of speaking style for the same or different input speech.

Claims22

Dependent claims

A linguistic content and speaking style disentanglement model, the model comprising:

a content encoder configured to:
receive, as input, input speech;
and
generate, as output, a latent representation of linguistic content for the input speech, the content encoder trained to disentangle speaking style information from the latent representation of linguistic content;
a style encoder configured to:
receive, as input, the same or different input speech;
and
generate, as output, a latent representation of speaking style for the same or different input speech, the style encoder trained to disentangle linguistic content information from the latent representation of speaking style;
and
a decoder configured to generate output speech based on the latent representation of linguistic content for the input speech and the latent representation of speaking style for the same or different input speech.

7 more elements

A computer-implemented method when executed on data processing hardware causes the data processing hardware to perform operations comprising:

receiving input speech;
processing, using a content encoder, the input speech to generate a latent representation of linguistic content for the input speech, wherein the content encoder is trained to disentangle speaking style information from the latent representation of linguistic content;
processing, using a style encoder, the same or different input speech to generate a latent representation of speaking style for the same or different input speech, wherein the style encoder is trained to disentangle linguistic content information from the latent representation of speaking style;
and
processing, using a decoder, the latent representation of linguistic content for the input speech and the latent representation of speaking style for the same or different input speech to generate output speech.

2 more elements

Dependencies are inferred from the claim text.

Unsupervised Learning of Disentangled Speech Content and Style Representation

Life of this patent

Unsupervised Learning of Disentangled Speech Content and Style Representation

Life of this patent

Abstract

Claims22