Voiceprint recognition method and device based on empirical mode decomposition and MFCC

Abstract

The embodiment of the invention discloses a voiceprint recognition method and device based on empirical mode decomposition and the mel frequency cepstrum coefficient (MFCC), and relates to the technical field of speech signal processing and computers. According to the method, the instantaneous feature parameter of a speech signal after empirical mode decomposition is extracted and fused with the traditional Meier feature parameter to form an improved feature parameter for voiceprint recognition. The device includes a data acquisition module, a high-speed data transmission module, an algorithmimplementation module, a data storage module and an user interface module. The device and the method can improve the efficiency and accuracy of identity authentication, the noise robustness of a system is improved, the response time is shortened, and meanwhile better user experience is brought.

Description

technical field [0001] The invention relates to the technical field of voice signal processing, in particular to a voiceprint recognition method and device based on empirical mode decomposition and MFCC. Background technique [0002] Voiceprint recognition technology is an important part of information science and artificial intelligence. It is divided into two aspects: speaker confirmation technology and speaker recognition technology. It is usually understood that speaker recognition technology is often used in criminal investigation, criminal tracking, and national defense. Listening, personalized applications, etc., speaker confirmation technology is often used in securities transactions, bank transactions, public security evidence collection, personal computer voice-activated locks, car voice-activated locks, identification of ID cards, credit cards, etc. There are two key issues in voiceprint recognition technology, one is feature extraction, and the other is pattern m...

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Problems solved by technology

[0004] However, MFCC mainly describes the characteristics of the vocal tract, and the semantic information contained in it is often stronger than the voiceprint information, and it has been observed that when MFCC is used as a feature, under actual challenging test conditions, such as noise, the system performance appears obvious. Degradation; at the same time, the current voiceprint system device built with MFCC features has a large delay in implementation, and there are also deficiencies in user voiceprint feature access, which will affect system performance and user experience

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