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Recently, the speaker clustering model based on aggregation hierarchy cluster(AHC) is a common method to solve two main problems: no preset category numberclustering and fix category number clustering. In general, model takes featureslike i-vectors as input of probability and linear discriminant analysis model(PLDA) aims to form the distance matric in long voice application scenario, andthen clustering results are obtained through the clustering model. However,traditional speaker clustering method based on AHC has the shortcomings oflong-time running and remains sensitive to environment noise. In this paper, wepropose a novel speaker clustering method based on Mutual Information (MI) anda non-linear model with discrete variable, which under the enlightenment ofTied Variational Autoencoder (TVAE), to enhance the robustness against noise.The proposed method named Discrete Tied Variational Autoencoder (DTVAE) whichshortens the elapsed time substantially. With experience results, itoutperforms the general model and yields a relative Accuracy (ACC) improvementand significant time reduction.

A Robust Speaker Clustering Method Based on Discrete Tied Variational Autoencoder