Many-to-many speaker conversion method based on improved STARGAN and x vector

Abstract

The invention discloses a many-to-many speaker conversion method based on an improved STARGAN and an x vector. The method comprises a training stage and a transforming stage; a speech conversion system is achieved via the combination of the improved STARGAN and the x vector; the method is the further improvement of STARGAN in a speech conversion application; a two-step antagonistic loss is provided and can effectively solve the problem of excessive smoothness caused by cyclic consistence loss expressed by L1; furthermore, a generator adopts a 2-1-2D CNN network, a semantic learning capacity and a speech spectrum synthesis capacity of the model can be well enhanced, and the problem of relatively poor speech similarity and naturalness after conversion in the STARGAN is overcome. Meanwhile, the x vector has better representation performance for short speech, the personality feature of the speaker can be fully represented, and a high quality many-to-many speech conversion method under thecondition of non-parallel text is achieved.

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 improved STARGAN and x-vector. Background technique [0002] Speech conversion is a research branch in the field of speech signal processing, which is developed and extended on the basis 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 persons 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), Recurrent Neural Network (RNN), and Deep Neural Networks (DNN). However, most of these speech conversion methods require that the...

AI Technical Summary

Problems solved by technology

[0006] Purpose of the invention: the technical problem to be solved by the present invention is to provide a many-to-many speaker conversion method based on the improved STARGAN and x-vector, and the proposed two-step adversarial loss can effectively solve the problem caused by using L1 due to the cycle consistency loss Over-smoothing problem, and the generator adopts 2-1-2D CNN network, which can better improve the model's semantic learning ability and voice spectrum synthesis ability, and overcome the problem of poor voice similarity and naturalness after conversion in STARGAN. Reduce the semantic learning difficulty of the encoding network, improve the spectrum generation quality of the decoding network, and use the x-vector to fully represent the speaker's personalized features, effectively improving the personality similarity of the converted voice

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