Recording device clustering method based on Gaussian mean super vectors and spectral clustering

Abstract

The invention provides a recording device clustering method based on Gaussian mean super vectors and spectral clustering. The method comprises the steps that the Melch frequency cepstrum coefficient MFCC characteristic which characterizes the recording device characteristic is extracted from a speech sample; the MFCC characteristics of all speech samples are used as input, and a common background model UBM is trained through an expectation maximization EM algorithm; the MFCC characteristic of each speech sample is used as input, and UBM parameters are updated through a maximum posteriori probability MAP algorithm to acquire the Gaussian mixture model GMM of each speech sample; the mean vector of all Gaussian components of each GMM is spliced in turn to form a Gaussian mean super vector; a spectral clustering algorithm is used to cluster the Gaussian mean super vectors of all speech samples; the number of recording devices is estimated; and the speech samples of the same recording device are merged. According to the invention, the speech samples collected by the same recording device can be found out without knowing the prior knowledge of the type, the number and the like of the recording devices, and the application scope of the method is wide.

Description

technical field [0001] The invention relates to the technical fields of intelligent voice signal processing, pattern recognition and audio forensics, in particular to a recording device clustering method based on Gaussian mean supervector and spectral clustering. Background technique [0002] With the development of voice forensics technology, the identification of recording equipment based on voice samples has achieved good results, which is of great significance in judicial forensics. Voice evidence collected by recording equipment has become one of the common forms of evidence, and has been submitted to courts or other law enforcement agencies in large numbers, playing an important role in solving cases. [0003] In actual cases, the recognition result of the recording device may be affected due to factors such as loss of recording device label information, equipment damage, and uncertain device identification results, thereby reducing its recognition in court evidence co...

AI Technical Summary

Problems solved by technology

[0003] In actual cases, the recognition result of the recording device may be affected due to factors such as loss of recording device label information, equipment damage, and uncertain device identification results, thereby reducing its recognition in court evidence collection; on the other hand, when a judge When faced with a large number of voice samples submitted, the primary concern may not be the category of the recording device, but to know which voice samples come from the same recording device

At this time, the problem that the judge has to face becomes: how to estimate the number of recording devices used to collect voice samples and combine the voice samples of the same recording device without knowing any prior information of the recording devices

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