Speaker recognition method based on depth learning

Abstract

The invention discloses a speaker recognition method based on depth learning. The method comprises the following steps: S1) carrying out pre-emphasis and overlapping-type framing windowing on collected voice signals; S2) carrying out endpoint detection on the collected voice signals by utilizing a dual-threshold endpoint detection method based on short-time energy and short-time zero-crossing rate, and judging and indentifying the staring moment, transition stage , noise section and ending moment of the voice; S3) carrying out feature extraction on the voice signals; S4) forming a depth belief network model based on restricted boltzmann machine hierarchy, training the established depth belief network model by utilizing layer-by-layer greedy algorithm and with speaker voice feature parameters being combined, and adding a Softmax classifier to the top layer of the depth belief network model; and S5) inputting the voice features of a speaker to the depth belief network model obtained after being subjected to training, calculating the probability that the model outputs voice features similar to the voice features of the other speakers, and selecting the speaker corresponding to the maximum probability as recognition result.

Description

technical field [0001] The present invention relates to the technical field of speech processing, in particular to a speaker recognition method based on deep learning. Background technique [0002] Speaker recognition is usually called voiceprint recognition. Compared with other biometric technologies, it has the characteristics of natural convenience, high user acceptance, and low equipment cost. Speaker recognition technology has been widely used in identity verification, access control systems, human-computer interaction, forensic identification, communication networks, mobile terminals, banking systems, national defense and military and other fields. Speaker recognition technology mainly includes speech feature parameter extraction and speaker pattern classification. Speech feature extraction is to extract the speaker's speech features and vocal tract characteristics. At present, the mainstream feature parameters, including MFCC, LPCC, pitch period, etc., are all based...

AI Technical Summary

Problems solved by technology

[0006] According to the prior art, mostly a single speech feature is used in the process of speaker recognition, which cannot fully characterize the characteristics of the speaker’s vocal tract, has poor robustness, and the speaker recognition model adopted is usually artificially set feature parameters, and For problems such as inability to automatically learn deeper feature information and unsatisfactory recognition effects, the p

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