Deep Spiking Neural Network model for time-variant signals classification: A real-time speech recognition approach

Juan P. Dominguez-Morales, Qian Liu, Robert James, Daniel Gutierrez-Galan, Angel Jimenez-Fernandez, Simon Davidson, Steve Furber

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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.

Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
ISBN (Electronic)9781509060146
Publication statusPublished - 10 Oct 2018
Event2018 International Joint Conference on Neural Networks - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018


Conference2018 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2018
CityRio de Janeiro


  • audio processing
  • Convolutional Neural Networks
  • deep learning
  • neuromorphic hardware
  • speech recognition
  • Spiking Neural Networks


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