본문 바로가기
  • Home

An Open-Source AI-Based Multilingual Translation Lecture Script System for Global University Lectures

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2025, 30(12), pp.101~112
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 30, 2025
  • Accepted : December 2, 2025
  • Published : December 31, 2025

DongHyeon Shin 1 Oh-Gyu Kwon 2 ManKi Min 1 Minseo Yoon 1 DoHyun Oh 1 Jae-Woo Ryu 3 Young Deok Park 1

1영남대학교
2모비핀테크놀러지
3니어네트웍스

Accredited

ABSTRACT

We propose an open-source Artificial Intelligence (AI)-driven multilingual translation system that integrates real-time speech recognition and translation to resolve language barriers and misrecognition of technical terms in university lecture environments. The proposed system combines Whisper-based speech recognition, Bidirectional Encoder Representations from Transformers (BERT) and Levenshtein-distance based correction algorithm, and the DeltaLM multilingual translation model to automate the entire process from noise reduction and sentence refinement to context-aware translation and lecture script generation. In particular, a translation dataset containing domain-specific technical terms in the field of computer engineering is constructed and fine-tuned to improve translation precision in specialized domains. Experimental results show that the Word Error Rate (WER) decreases from 9.2 % to 4.7 %, achieving an improvement of approximately 51 %, while the average Bilingual Evaluation Understudy (BLEU) score increases from 56.3 to 60.2, corresponding to a 6.9 % performance gain. These results confirm that the proposed system achieves consistent translation quality improvements across all language pairs in academic lecture scenarios.

Citation status

* References for papers published after 2024 are currently being built.