  Naturalistic speech recordings usually contain speech signals from multiple
speakers. This phenomenon can degrade the performance of speech technologies
due to the complexity of tracing and recognizing individual speakers. In this
study, we investigate the detection of overlapping speech on segments as short
as 25 ms using Convolutional Neural Networks. We evaluate the detection
performance using different spectral features, and show that pyknogram features
outperforms other commonly used speech features. The proposed system can
predict overlapping speech with an accuracy of 84\% and Fscore of 88\% on a
dataset of mixed speech generated based on the GRID dataset.
