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

Orthonormal Embedding-based Deep Clustering for Single-channel Speech Separation