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