  Deep clustering is a deep neural network-based speech separation algorithm
that first trains the mixed component of signals with high-dimensional
embeddings, and then uses a clustering algorithm to separate each mixture of
sources. In this paper, we extend the baseline criterion of deep clustering
with an additional regularization term to further improve the overall
performance. This term plays a role in assigning a condition to the embeddings
such that it gives less correlation to each embedding dimension, leading to
better decomposition of the spectral bins. The regularization term helps to
mitigate the unavoidable permutation problem in the conventional deep
clustering method, which enables to bring better clustering through the
formation of optimal embeddings. We evaluate the results by varying embedding
dimension, signal-to-interference ratio (SIR), and gender dependency. The
performance comparison with the source separation measurement metric, i.e.
signal-to-distortion ratio (SDR), confirms that the proposed method outperforms
the conventional deep clustering method.
