Deception voice detection method based on deep neural network

Abstract

The invention discloses a deception voice detection method based on a deep neural network. The method comprises the following steps: A, training and building a deception voice detection model based onthe deep neural network according to voice data, with the known authenticity, of a user, wherein the deception voice detection model has network parameters; and B, performing classification judgmenton test voices to be tested in the trained deception voice detection model with the network parameters so as to judge whether the test voices are real voices or deceptive voices. The method has the following advantages: novel unknown voice synthesis, voice conversion, record playback and other deceptive attacks are supported.

Description

technical field

[0001] The invention relates to the technical field of computer information services, in particular to a method for detecting fraudulent voice based on a deep neural network. Background technique

[0002] Speaker recognition is to identify a person's identity from the speaker's voice. In layman's terms, it is answering the question "Who is talking?" Specifically, the distinguishable voiceprint representation of the individual is extracted from the speaker's voice, and the representation is used as the speaker's identity information to achieve identification. In practical application scenarios, speaker recognition technology, like other identity verification technologies, is accompanied by artificial malicious deception attacks, which has security problems.

[0003] Currently, there are three main spoofing attack modes:

[0004] (1) Deliberate imitation from other speakers (such as ventriloquist skills);

[0005] (2) Natural speech synthesized by high-qual...

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