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

Boyuan Zhang, Xibang Yang, Tong Xie, Shuyuan Zhu, Bing Zeng

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Boyuan ZhangSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.ORCID 0000-0003-1766-5415

Xibang YangSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.ORCID 0009-0006-4310-7424

Tong XieNanyang Technological University, 50 Nanyang Avenue Avenue, Singapore 639798, Singapore.ORCID 0000-0003-2999-622X

Shuyuan ZhuSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.ORCID 0000-0003-4450-3868

Bing ZengSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.ORCID 0000-0002-4491-7967

Funding

National Natural Science Foundation of China under Grant 62401518

6 · The paper itself

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.

Indexed as

deep learning network (DLN)filamentary convolutionfrequency-level feature extractionspoken language identification

Identifiers

PMID40431873

DOI10.3390/s25103085

PMCPMC12115191

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.