The intent of this work is to demonstrate how deep learning techniques can be successfully used to detect overlapped speech on independent short timeframes. A secondary objective is to provide an understanding on how the duration of the signal frame influences the accuracy of the method. We trained a deep neural network with heterogeneous layers and obtained close to 80% inference accuracy on frames going as low as 25 milliseconds. The proposed system provides higher detection quality than existing work and can predict overlapped speech with up to 3 simultaneous speakers. The method exposes low response latency and does not require a high amount of computing power.