Ponencia
Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach
Autor/es | Domínguez Morales, Juan Pedro
Liu, Qian James, Robert Gutiérrez Galán, Daniel Jiménez Fernández, Ángel Francisco Davidson, Simón Furber, Steve B. |
Departamento | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores |
Fecha de publicación | 2018 |
Fecha de depósito | 2020-01-22 |
Publicado en |
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ISBN/ISSN | 978-1-5090-6014-6 2161-4407 |
Resumen | 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 ... 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. |
Identificador del proyecto | TEC2016-77785-P |
Cita | Domínguez Morales, J.P., Liu, Q., James, R., Gutiérrez Galán, D., Jiménez Fernández, Á.F., Davidson, S. y Furber, S. B. (2018). Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach. En IJCNN 2018 : International Joint Conference on Neural Networks Rio de Janeiro, Brazil: IEEE Computer Society. |
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