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Multi-scale chunked residual encoding and temporal stochastic interpolation padding in SNNs for enhanced speech

Qi Zhang1, Huamin Wang1, Hangchi Shen1

  • 1Southwest University College of Artificial Intelligence, Chongqing, 400715, Chongqing, China; Yibin Academy of Southwest University, Yibin, 644000, Sichuan, China; Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips, Chongqing, 400715, Chongqing, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 27, 2026
PubMed
Summary

This study introduces novel methods for Spiking Neural Networks (SNNs) to improve speech processing. The new techniques enhance accuracy and significantly reduce energy consumption in SNN models.

Keywords:
Multi-Scale temporal informationResidual connectionsSpeech classificationSpiking neural networks

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Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Signal Processing

Background:

  • Spiking Neural Networks (SNNs) offer potential for temporal data processing, particularly in speech.
  • Current SNN encoding mechanisms struggle with multi-scale speech feature extraction.
  • Temporal dimension mismatches limit the use of residual connections in SNNs.

Purpose of the Study:

  • To enhance the extraction of multi-scale temporal information from speech datasets using SNNs.
  • To enable the effective application of residual connections in spiking architectures.
  • To improve the overall performance and robustness of SNNs for speech processing.

Main Methods:

  • Proposed the Multi-Scale Chunked Residual Encoder (MCRE) for parallel local and global feature processing.
  • Introduced Temporal Stochastic Interpolation Padding (TSIP) using Gaussian distribution for sequence length equalization and residual connection enablement.
  • MCRE mimics hippocampal-cortical information reorganization for enhanced contextual understanding.

Main Results:

  • Achieved state-of-the-art (SOTA) accuracy of 96.44% on the Spiking Heidelberg Digits (SHD) dataset, a 1.34% improvement.
  • Improved performance on Spiking Speech Commands (SSC) with 80.92% accuracy (+0.63%) and Google Speech Commands v0.02 (GSC) with 95.64% accuracy (+0.29%).
  • Demonstrated significant energy consumption reduction, up to 55%, compared to baseline models.

Conclusions:

  • The proposed MCRE and TSIP effectively address limitations in SNN speech processing, enhancing feature extraction and enabling residual connections.
  • The novel methods lead to improved accuracy and robustness in SNNs for speech tasks.
  • Significant reductions in energy consumption highlight the efficiency of the proposed approach for SNN applications.