Voice generation method and device based on generative adversarial network

Abstract

The invention discloses a voice generation method based on a generative adversarial network. According to the method, randomly-generated noise data meeting Gaussian distribution is converted into a simulation sample through a generative model; as the simulation sample does not have the language content, when the generative model and a discrimination model are circularly updated, generative capacities required to be learned by the generative model and discrimination capacities required to be learned by the discrimination model are correspondingly increased, and accordingly the generative capacities of the generative model and the discrimination capacities of the discrimination model are improved; when a contrast value between a training sample and the simulation sample is smaller than or equal to a preset threshold value, it is thought that the generative model has the capacity of generating real data; a voice database generated through the generative model has enough reality, and the recognition rate can be increased when the generative model is applied to identity recognition. Correspondingly, the voice generation method, a voice generation device and voice generation equipment based on the generative adversarial network and a computer readable storage medium have the same advantages.

Description

technical field [0001] The present invention relates to the field of audio technology, and more specifically, to a speech generation method, device, equipment and computer-readable storage medium based on a generative confrontation network. Background technique [0002] With the continuous development of computer technology, audio processing technology has also become quite mature, and voiceprint recognition technology is a common technology used to identify users. The key of voiceprint recognition technology lies in the extraction of voice features and the construction of voice database. [0003] At present, the method of building a speech database based on the generative confrontation network in the existing technology is: collect the speech of the same sentence spoken by different characters, use the speech of one of the characters as a training sample, and use the speech of the other characters as a simulation sample; The generation model and the discriminant model are ...

AI Technical Summary

Problems solved by technology

[0004] Since the speech content of the training sample and the simulation sample used in the prior art are the same, and they have similar speech features on the frequency spectrum, the function of the discriminant model is only to simply distinguish the sound quality and timbre of the training sample and the simulated sample, resulting in the discriminant model In the process of cyclic update, less discriminative ability needs to be learned, which makes the discriminative effect unsatisfactory, and also limits the simulation generation ability of the generative model, thereby reducing the authenticity of the voice data generated by the generative model and the ability to identify users. recognition ability

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