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CL-CNN: A Low-Resource Character-Level CNN for Bangladeshi Ethnic Language Recognition

Students & Supervisors

Student Authors
Sowhanur Rahman Nirob
Bachelor of Science in Computer Science & Engineering, FST
Priya Rani Das
Bachelor of Science in Computer Science & Engineering, FST
Md Tanzeem Rahat
Master of Science in Computer Science, FST
Supervisors
Sazia Sharmin
Lecturer, Faculty, FST
Prof. Dr. Kamruddin Nur
Professor, Faculty, FST

Abstract

Bangladesh hosts a diverse set of ethnolinguistic communities; however, most NLP research has centered on Bangla, leaving minority languages underrepresented in digital resources. We study the automatic identification of ethnic languages using a lightweight character-level Convolutional Neural Network (CNN) designed for low-resource settings. Using the “Bd Ethnic Language Classification” dataset (Kag- gle), we constructed a character vocabulary and trained a 1D CNN with global max pooling and dense layers. Due to severe class sparsity, the Tripura subset (93 samples) was excluded to avoid extreme imbalance, and our target set comprised Chakma, Marma, Santali, Garo, and Rakhine. The proposed CNN attains 95.23% accuracy, 95.35% precision, 95.24% recall, and 95.23% F1, outperforming classical and neural baselines (SVM, Random Forest, BiLSTM). We an- alyze confusion patterns (e.g., Chakma-Marma) and discuss the implications for inclusive NLP, accessibility, and cultural preservation. Our study established a strong baseline for low-resource ethnic language identification and highlighted practical design choices for robust character-level models in multilingual environments.

Keywords

ethnic language identification natural language processing character-level CNN low-resource languages.

Publication Details

  • Type of Publication:
  • Conference Name: 2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking
  • Date of Conference: 16/04/2026 - 16/04/2026
  • Venue: IT Business Incubator, Chittagong University of Engineering and Technology (CUET), Chattogram, Bangladesh