北京交通大学自动化与智能学院

郏东耀课题组论文发表于《Applied Sciences》

发布于 2026-01-16

    我所郏东耀老师课题组完成的论文《Tongan Speech Recognition Based on Layer-Wise Fine-Tuning Transfer Learning and Lexicon Parameter Enhancement》于2025-10在《Applied Sciences》正式发表。论文摘要如下:

Tongan Speech Recognition Based on Layer-Wise Fine-Tuning Transfer Learning and Lexicon Parameter Enhancement
Tongan Speech Recognition Based on Layer-Wise Fine-Tuning Transfer Learning and Lexicon Parameter Enhancement

Abstract: Speech recognition, as a key driver of artificial intelligence and global communication, has advanced rapidly in major languages, while studies on low-resource languages remain limited. Tongan, a representative Polynesian language, carries significant cultural value. However, Tongan speech recognition faces three main challenges: data scarcity, limited adaptability of transfer learning, and weak dictionary modeling. This study proposes improvements in adaptive transfer learning and NBPE-based dictionary modeling to address these issues. An adaptive transfer learning strategy with layer-wise unfreezing and dynamic learning rate adjustment is introduced, enabling effective adaptation of pretrained models to the target language while improving accuracy and efficiency. In addition, the MEA-AGA is developed by combining the Mind Evolutionary Algorithm (MEA) with the Adaptive Genetic Algorithm (AGA) to optimize the number of byte-pair encoding (NBPE) parameters, thereby enhancing recognition accuracy and speed. The collected Tongan speech data were expanded and preprocessed, after which the experiments were conducted on an NVIDIA RTX 4070 GPU (16 GB) using CUDA 11.8 under the Ubuntu 18.04 operating system. Experimental results show that the proposed method achieved a word error rate (WER) of 26.18% and a word-per-second (WPS) rate of 68, demonstrating clear advantages over baseline methods and confirming its effectiveness for low-resource language applications. Although the proposed approach demonstrates promising performance, this study is still limited by the relatively small corpus size and the early stage of research exploration. Future work will focus on expanding the dataset, refining adaptive transfer strategies, and enhancing cross-lingual generalization to further improve the robustness and scalability of the model.

引用信息:Junhao Geng, Dongyao Jia, Ziqi Li, Zihao He, Nengkai Wu, Weijia Zhang et al.. Tongan Speech Recognition Based on Layer-Wise Fine-Tuning Transfer Learning and Lexicon Parameter Enhancement. Applied Sciences, 2025-10-24. DOI: 10.3390/app152111412

论文链接:https://doi.org/10.3390/app152111412