Live demonstration: Event-driven real-time spoken digit recognition system

Summary form only given. We previously described a deep network system that reached an accuracy of 82% on a digit recognition task using the spike outputs from a Dynamic Audio Sensor (DAS) in response to audio samples from the TIDIGITS database. The audio samples were played directly to the system therefore bypassing the microphones. This work presents an interactive real-time demonstration of this digit recognition system. The system classifies a spoken digit based on the output spikes of the DAS in response to digits spoken into the on-board microphones.