Neuromorphic-Inspired Language Identification for Low-Resource Code-Switched Texts Using Spiking Neural Networks

Multilingual code-switched language identification remains a challenging task due to frequent language alternation, lexical ambiguity, and the limited availability of annotated corpora for low-resource languages. While transformer-based language models have demonstrated strong performance, their computational complexity motivates the exploration of more efficient neuromorphic approaches. This study proposes an optimized Spiking Neural Network (SNN) framework for multilingual code-switched language identification using spike-based neural computation. Text data are preprocessed and represented using Term Frequency–Inverse Document Frequency (TF-IDF) feature vectors, which are transformed into temporal spike trains through a rate-coding mechanism over a fixed simulation window. The encoded spike sequences are processed by a feedforward SNN employing Leaky Integrate-and-Fire (LIF) neurons. Hyperparameters are optimized using Optuna to improve classification performance. The proposed model is evaluated on a balanced multilingual code-switched dataset and compared with classical machine learning models, including Logistic Regression, Support Vector Machine, and Random Forest, as well as deep learning and transformer-based models, including BiLSTM, mBERT, AfroXLMR, and XLM-RoBERTa. Experimental results demonstrate that the optimized SNN achieves an overall classification accuracy of 81%, outperforming the baseline SNN while remaining competitive with several state-of-the-art neural models. Statistical validation using the Friedman and Nemenyi tests confirms significant performance differences among the evaluated classifiers while demonstrating that the optimized SNN performs competitively against several strong baselines. Although transformer models achieve the highest overall accuracy, the proposed SNN offers a computationally efficient neuromorphic alternative that combines temporal spike processing with stable learning behaviour for multilingual code-switched language identification. These findings demonstrate the potential of spike-based neural computing for low-resource multilingual natural language processing and provide a reproducible foundation for future neuromorphic language identification research.