Speaker identification method based on deep stack autoencoder network

Abstract

The invention relates to a speaker identification method based on the deep stack autoencoder network. The method comprises steps of S1, speaker feature extraction; S2, stack autoencoder network design; and S3: speaker identification and decision making. The method is advantaged in that compared with traditional speaker identification, the deep stack autoencoder network is fused with a speaker identification system model, in combination with the multi-layer structure of a stack autoencoder to improve the characterization ability of an evaluation model, system identification performance in the presence of background noise can be finitely improved, influence of the noise on the system performance is reduced, system noise robustness is improved, the system structure is optimized, and identification timeliness is effectively enhanced.

Description

technical field

[0001] The invention relates to the technical field of computer vision, in particular to a speaker recognition method based on a deep stack autoencoder network. Background technique

[0002] Speaker recognition, also known as voiceprint recognition, is a biometric authentication technology that uses specific speaker information contained in voice signals to identify the identity of the speaker. In recent years, the introduction of the identity vector (i-vector) speaker modeling method based on factor analysis has significantly improved the performance of the speaker recognition system. I-vector uses a low-dimensional total variable space to represent the speaker subspace and channel subspace, and maps the speaker's voice to this space to obtain a fixed-length vector representation (i.e., i-vector). The speaker recognition system based on i-vector mainly includes three steps: extraction of sufficient statistics, i-vector mapping, and calculation of likelihood...

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