Filamentary Convolution for Spoken Language Identification: A Brain-Inspired Approach

Abstract

Spoken language identification (SLI) by human beings relies on the hierarchical understanding of one or a few words within the voice signal, encapsulated within the corresponding time windows. Concurrently, frequency-domain features play a crucial role in enhancing identification. The short-time Fourier transform (STFT) has conventionally served as a pivotal component in the forefront of most SLI systems, including deep-learning networks (DLNs). Nevertheless, the use of rectangle-shaped masks in STFT introduces spectral component mixing across different time windows, potentially resulting in an aliasing effect. To address this limitation, we propose a novel filamentary convolution framework to replace the conventional rectangle-shaped convolutions. This framework not only reduces complexity but also enhances feature learning within each frame. Leveraging filamentary convolution, we formulate an encoding module with a non-overlapping strategy and a multi-level information extraction (MIE) module featuring unbalanced dual-route convolution (UDRC) blocks. The frequency features learned from filamentary convolutions are seamlessly integrated through a long-short term memory (LSTM) structure. In summary, our decision-making process employs the filamentary convolution kernel-based hierarchical neural network (FCK-NN), comprising an encoding module, MIE module, and LSTM module. We conduct experiments on a novel dataset encompassing 44 languages, curated by ourselves, and the results validate that our FCK-NN yields a significant improvement in performance.