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Bio-inspired Multi-layer Spiking Neural Network Extracts Discriminative Features from Speech Signals

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10639))

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Abstract

Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which can subsequently be used in a classifier. The architecture consists of a spiking convolutional/pooling layer followed by a fully connected spiking layer for feature discovery. The convolutional layer of leaky, integrate-and-fire (LIF) neurons represents primary acoustic features. The fully connected layer is equipped with a probabilistic spike-timing-dependent plasticity learning rule. This layer represents the discriminative features through probabilistic, LIF neurons. To assess the discriminative power of the learned features, they are used in a hidden Markov model (HMM) for spoken digit recognition. The experimental results show performance above 96% that compares favorably with popular statistical feature extraction methods. Our results provide a novel demonstration of unsupervised feature acquisition in an SNN.

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Author information

Authors and Affiliations

  1. The Center for Advanced Computer Studies, Bio-inspired AI Lab, University of Louisiana at Lafayette, Lafayette, LA, 70503, USA

    Amirhossein Tavanaei & Anthony Maida

Authors
  1. Amirhossein Tavanaei
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  2. Anthony Maida
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Corresponding author

Correspondence to Amirhossein Tavanaei .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Tavanaei, A., Maida, A. (2017). Bio-inspired Multi-layer Spiking Neural Network Extracts Discriminative Features from Speech Signals. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_95

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  • DOI: https://doi.org/10.1007/978-3-319-70136-3_95

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70135-6

  • Online ISBN: 978-3-319-70136-3

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