Deep Spiking Neural Network model for time variant signals classification: a real time speech recognition approach
Speech recognition has become an important task to improve the human machine interface. Taking into account the limitations of current automatic speech recognition systems, like non real time cloud based solutions or power demand, recent interest for neural networks and bio inspired systems has motivated the implementation of new techniques. Among them, a combination of spiking neural networks and neuromorphic auditory sensors offer an alternative to carry out the human like speech processing task. In this approach, a spiking convolutional neural network model was implemented, in which the weights of connections were calculated by training a convolutional neural network with specific activation functions, using firing rate based static images with the spiking information obtained from a neuromorphic cochlea. The system was trained and tested with a large dataset that contains "left" and "right" speech commands, achieving 89.90% accuracy. A novel spiking neural network model has been proposed to adapt the network that has been trained with static images to a non static processing approach, making it possible to classify audio signals and time series in real time.
2018 International Joint Conference on Neural Networks (IJCNN)