Speaker Clustering Algorithm Based on Feature Fusion

doi:10.12068/j.issn.1005-3026.2021.07.007

Abstract

Abstract: Aiming at the limitation of single acoustic feature and k-means algorithm in speaker clustering technology， in order to better express the speaker’s personality information and improve the accuracy of speaker clustering， feature fusion and AE-SOM neural network are applied to speaker clustering， and an improved speaker clustering algorithm is proposed. The algorithm combines MFCC feature parameters with LPCC feature parameters to improve the speaker’s personality information. The AE-SOM neural network is added on the basis of k-means to reduce the dimension of input features， determine the number of speakers and select the cluster centers， so as to make up for the defects of k-means algorithm. Simulation results show that the improved clustering algorithm can effectively improve the accuracy of speaker clustering.

Key words: acoustic feature; k-means; speaker clustering; feature fusion; AE-SOM; neural network

CLC Number:

TN912.3

ZHENG Yan， JIANG Yuan-xiang. Speaker Clustering Algorithm Based on Feature Fusion[J]. Journal of Northeastern University(Natural Science), 2021, 42(7): 952-959.

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