Automatic recognition of digital speech using deep spiking neural network based on fuzzy weighting

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Hamian Melika; Faez Karim; Nazari Sohila; Sabeti Maliheh

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Journal Paper

Paper Information

Journal: Journal of Circuits, Data and System Analysis Year:2024 | Volume:2 | Issue:2 (پیاپی 6) Page(s): 10-18

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Title

Automatic recognition of digital speech using deep spiking neural network based on fuzzy weighting

Author(s)

Hamian Melika | Faez Karim | Nazari Sohila | Sabeti Maliheh | Issue Writer Certificate

Keywords

digit speech recognition

spiking neural network (SNN)

fuzzy weighting system (FWS)

Abstract

Despite the progress made in the design of spiking neural networks (SNN), training these systems for classification and artificial intelligence applications is one of the upcoming challenges for their design. In this paper, we have investigated supervised learning in SNNs for the problem of digit recognition and classification from speech signals. SNN training is done using fuzzy logic. In this method, the learning rule integrates Fuzzy Weighting System (FWS) with Spike Time Dependent Flexibility (STDP). SNN uses a set of training neurons with fuzzy weighting to reduce the number of weights of each neuron in the training phase, in which the data related to all classes are fed to these neurons to determine the training weights and threshold estimation with the help of the Wild Horse Algorithm (WHO). Then, these rule weights are given to the neurons of different layers to reflect the similarities in the extracted features among the classes as an objective function. A case study has been carried out on a set of audio signal data for digit classification. Our network achieved a classification accuracy of 98.17% on the TIDIGITS test database.

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