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

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

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 mixi...

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Bibliographic Details
Main Authors: Boyuan Zhang, Xibang Yang, Tong Xie, Shuyuan Zhu, Bing Zeng
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Sensors
Subjects:
spoken language identification
deep learning network (DLN)
filamentary convolution
frequency-level feature extraction
Online Access:https://www.mdpi.com/1424-8220/25/10/3085
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Summary: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.
ISSN:1424-8220

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