Voice conversion method and system

Patent metadata

Field	Value
Publication number	US-8930183-B2
Application number	US-201113217628-A
Country	US
Kind code	B2
Filing date	Aug 25, 2011
Priority date	Mar 29, 2011
Publication date	Jan 6, 2015
Grant date	Jan 6, 2015

Abstract

A method of converting speech from the characteristics of a first voice to the characteristics of a second voice, the method comprising:
receiving a speech input from a first voice, dividing said speech input into a plurality of frames; mapping the speech from the first voice to a second voice; and outputting the speech in the second voice, wherein mapping the speech from the first voice to the second voice comprises, deriving kernels demonstrating the similarity between speech features derived from the frames of the speech input from the first voice and stored frames of training data for said first voice, the training data corresponding to different text to that of the speech input and wherein the mapping step uses a plurality of kernels derived for each frame of input speech with a plurality of stored frames of training data of the first voice.

First claim

The invention claimed is:

1. A method of converting speech from the characteristics of a first voice to the characteristics of a second voice, the method comprising:
receiving a speech input from a first voice, dividing said speech input into a plurality of frames;
in a processor, mapping the speech from the first voice to a second voice using a Gaussian process; and
outputting the speech in the second voice,
wherein mapping the speech from the first voice to the second voice comprises, deriving kernels demonstrating the similarity between speech features derived from the frames of the speech input from the first voice and stored frames of training data for said first voice, the training data corresponding to different text to that of the speech input and wherein the mapping step uses a plurality of kernels derived for each frame of input speech with a plurality of stored frames of training data of the first voice and using said plurality of kernels to define a non-parametric Gaussian process prior for said mapping.

2. A method according to claim 1 , wherein kernels are derived for both static and dynamic speech features.

3. A method according to claim 1 , wherein the speech to be output is determined according to a Gaussian Process predictive distribution:
p ( y t |x t ,x*,y *, )= (μ( x t ),Σ( x t )),

where y t is the speech vector for frame t to be output, x t is the speech vector for the input speech for frame t, x*, y* is {x 1 *, y 1 *}, . . . , {x N *, y N *}, where x t * is the t-th frame of training data for the first voice and y t * is the t-th frame of training data for the second voice, M denotes the model, μ(x t ) and Σ(x t ) are the mean and variance of the predictive distribution for given x t .

4. A method according to claim 3 , wherein

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Assignees

• Chun Byung Ha
• Gales Mark John Francis
• Toshiba Kk

Inventors

• Chun Byung Ha
• Gales Mark John Francis

Classifications

• G10L21/007

  characterised by the process used · CPC title

• G10L13/033

  Voice editing, e.g. manipulating the voice of the synthesiser · CPC title

• G10L2021/0135

  Voice conversion or morphing · CPC title

• G10L21/00

  Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility (G10L19/00 takes precedence) · CPC title

• G10L15/063

  Training · CPC title