Unsupervised Speech Separation Using Statistical, Auditory and Signal Processing Approaches

Unsupervised speech separation refers to the task of separating the individual speaker's speech from the multi- speaker speech without using any apriori information regarding speakers. This paper mainly focuses on unsupervised speech separation for single and multichannel case. State of art speech separation algorithms based on statistical, auditory, and signal processing approaches are evaluated and results are discussed. Algorithms are evaluated for synthetic and real speech mixtures. Experimental results shows that multichannel speech separation algorithms perform better than single channel for artificial speech mixtures and for real speech mixtures the efficacy of signal processing approach compared with other two in terms of subjective evaluation.