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In certain applications such as zero-resource speech processing
or very-low resource speech-language systems, it might
not be feasible to collect speech activity detection (SAD) annotations.
However, the state-of-the-art supervised SAD techniques
based on neural networks or other machine learning
methods require annotated training data matched to the target
domain. This paper establish a clustering approach for fully
unsupervised SAD useful for cases where SAD annotations
are not available. The proposed approach leverages Hartigan