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A multi-to-many speaker conversion method based on i-vector under the condition of non-parallel text

A conversion method and speaker technology, applied in speech analysis, speech synthesis, instruments, etc., can solve the problems of unimproved speech similarity, unable to fully express the speaker's personalized characteristics, etc., so as to improve the personality similarity and speech Quality, improving versatility and practicality, and the effect of good application prospects

Active Publication Date: 2021-01-26
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, since VAWGAN still only uses the speaker's identity label to build a voice conversion system, and the speaker's identity label cannot fully express the individual characteristics of the speaker, the voice similarity of the converted voice has not been improved.

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  • A multi-to-many speaker conversion method based on i-vector under the condition of non-parallel text
  • A multi-to-many speaker conversion method based on i-vector under the condition of non-parallel text
  • A multi-to-many speaker conversion method based on i-vector under the condition of non-parallel text

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Embodiment Construction

[0037] Such as figure 1 As shown, the high-quality speech conversion method of the present invention is divided into two parts: the training part is used to obtain the parameters and conversion functions required for speech conversion, and the conversion part is used to convert the source speaker's voice into the target speaker's voice.

[0038] The implementation steps of the training phase are:

[0039] 1.1) Obtain the training corpus of non-parallel text, the training corpus is the corpus of multiple speakers, including the source speaker and the target speaker. The training corpus is taken from the VCC2018 speech corpus. There are 4 male and 4 female speakers in the training set of this corpus, and each speaker has 80 sentence corpus. This method can realize conversion under parallel text, and can also realize conversion under non-parallel text, so these training corpora can also be non-parallel text.

[0040] 1.2) The training corpus uses the WORLD speech analysis / synt...

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Abstract

The invention discloses a multi-to-many speaker conversion method based on i vector under the condition of non-parallel text, which includes a training stage and a conversion stage. The combination of VAWGAN and i vector is used to realize a speech conversion system, which can better improve the conversion. The personality similarity and voice quality of the post-voice are realized, and a high-quality voice conversion method is realized. In addition, the method removes the dependence on parallel text, realizes the speech conversion under the condition of non-parallel text, and the training process does not require any alignment process, and the method can also integrate the conversion systems of multiple source-target speaker pairs into one In the conversion model, multi-speaker-to-multi-speaker conversion is realized. This method has good application prospects in the fields of cross-language voice conversion, movie dubbing, and voice translation.

Description

technical field [0001] The invention relates to a many-to-many speaker conversion method, in particular to a multi-to-many speaker conversion method based on i vector under non-parallel text conditions. Background technique [0002] Speech conversion is a research branch in the field of speech signal processing, which is carried out and developed on the basis of the research of speech analysis, recognition and synthesis. The goal of voice conversion is to change the voice personality of the source speaker so that it has the voice personality of the target speaker, that is, to make the voice spoken by one person sound like another person's voice after conversion, while preserving semantics . [0003] After years of research on voice conversion technology, many classic conversion methods have emerged. These include most voice conversion methods such as Gaussian Mixed Model (GMM), frequency bending, and deep neural network (DNN). However, most of these speech conversion meth...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G10L13/08G10L19/02G10L21/007G10L13/02G10L25/30
CPCG10L13/02G10L13/08G10L19/02G10L21/007G10L25/30
Inventor 李燕萍左宇涛张燕
Owner NANJING UNIV OF POSTS & TELECOMM
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