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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Filamentary Convolution for SLI: A Brain-Inspired Approach with High Efficiency

by
Boyuan Zhang
1,
Xibang Yang
1,
Tong Xie
2,
Shuyuan Zhu
1 and
Bing Zeng
1,*
1
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2
Nanyang Technological University, 50 Nanyang Avenue Avenue, Singapore 639798, Singapore
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(10), 3085; https://doi.org/10.3390/s25103085
Submission received: 27 March 2025 / Revised: 6 May 2025 / Accepted: 8 May 2025 / Published: 13 May 2025
(This article belongs to the Section Sensor Networks)
Versions Notes

Abstract

Spoken language identification (SLI) relies on detecting key frequency characteristics like pitch, tone, and rhythm. While the short-time Fourier transform (STFT) generates time–frequency acoustic features (TFAF) for deep learning networks (DLNs), rectangular convolution kernels cause frequency mixing and aliasing, degrading feature extraction. We propose filamentary convolution to replace rectangular kernels, reducing the parameters while preserving inter-frame features by focusing solely on frequency patterns. Visualization confirms its enhanced sensitivity to critical frequency variations (e.g., intonation, rhythm) for language recognition. Evaluated via self-built datasets and cross-validated with public corpora, filamentary convolution improves the low-level feature extraction efficiency and synergizes with temporal models (LSTM/TDNN) to boost recognition. This method addresses aliasing limitations while maintaining computational efficiency in SLI systems.
Keywords: spoken language identification; deep learning network (DLN); filamentary convolution; frequency-level feature extraction spoken language identification; deep learning network (DLN); filamentary convolution; frequency-level feature extraction

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MDPI and ACS Style

Zhang, B.; Yang, X.; Xie, T.; Zhu, S.; Zeng, B. Filamentary Convolution for SLI: A Brain-Inspired Approach with High Efficiency. Sensors 2025, 25, 3085. https://doi.org/10.3390/s25103085

AMA Style

Zhang B, Yang X, Xie T, Zhu S, Zeng B. Filamentary Convolution for SLI: A Brain-Inspired Approach with High Efficiency. Sensors. 2025; 25(10):3085. https://doi.org/10.3390/s25103085

Chicago/Turabian Style

Zhang, Boyuan, Xibang Yang, Tong Xie, Shuyuan Zhu, and Bing Zeng. 2025. "Filamentary Convolution for SLI: A Brain-Inspired Approach with High Efficiency" Sensors 25, no. 10: 3085. https://doi.org/10.3390/s25103085

APA Style

Zhang, B., Yang, X., Xie, T., Zhu, S., & Zeng, B. (2025). Filamentary Convolution for SLI: A Brain-Inspired Approach with High Efficiency. Sensors, 25(10), 3085. https://doi.org/10.3390/s25103085

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