  In this work, we propose an overlapped speech detection system trained as a
three-class classifier. Unlike conventional systems that perform binary
classification as to whether or not a frame contains overlapped speech, the
proposed approach classifies into three classes: non-speech, single speaker
speech, and overlapped speech. By training a network with the more detailed
label definition, the model can learn a better notion on deciding the number of
speakers included in a given frame. A convolutional recurrent neural network
architecture is explored to benefit from both convolutional layer's capability
to model local patterns and recurrent layer's ability to model sequential
information. The proposed overlapped speech detection model establishes a
state-of-the-art performance with a precision of 0.6648 and a recall of 0.3222
on the DIHARD II evaluation set, showing a 20% increase in recall along with
higher precision. In addition, we also introduce a simple approach to utilize
the proposed overlapped speech detection model for speaker diarization which
ranked third place in the Track 1 of the DIHARD III challenge.
