2025
doi: 10.12720/jait.16.10.1470-1478
Indo-ASR+: Enhancing Indonesian Automatic Speech Recognition by Fine-Tuning Wav2Vec2 with FAdam
Irfan Darmawan1
,
Alam Rahmatulloh2,* ,
Rohmat Gunawan2 ,
R. Wahjoe Witjaksono1 ,
Ghatan Fauzi Nugraha3
- 1Department of Information System, Telkom University, Bandung, Indonesia
- 2Department of Informatics, Siliwangi University, Tasikmalaya, Indonesia
- 3Siliwangi Artificial Intelligence Research Group, Siliwangi University, Tasikmalaya, Indonesia
- Manuscript receivedMay 27, 2025
- revisedJune 19, 2025
- acceptedJuly 28, 2025
- publishedOctober 24, 2025
Abstract
Automatic Speech Recognition (ASR) has become a key technology in human-machine interaction, especially in supporting languages with limited resources such as Bahasa Indonesia. Although deep learning-based models such as Wav2Vec2 have shown good performance in speech recognition, further optimization is still needed to improve training accuracy and efficiency, especially in data-constrained and noisy environments. This research focuses on optimizing the Wav2Vec2 model for Indonesian ASR by applying the Fisher Adam (FAdam) optimizer. FAdam combines Natural Gradient Descent (NGD) with Fisher Information Matrix (FIM) to improve learning stability, accelerate convergence, and reduce sensitivity to noise in the data. The model was trained using the Indonesian Common Voice dataset and evaluated based on Word Error Rate (WER) of 5.59% and Character Error Rate (CER) of 1.76% on the validation set. Experimental results show that this approach not only improves accuracy over previous methods, also enhances training efficiency and improves the stability of model convergence compared to state-of-the-art models such as XLSR-53 and XLS-R 300m for Indonesian ASR. In addition, FAdam is shown to provide increased inference speed, making it a more optimal solution for ASR implementation in real-world scenarios. This research contributes to the development of a more adaptive and efficient ASR technology for Indonesian, while opening up further optimization opportunities in self-supervised learning-based models.
Keywords
- Automatic Speech Recognition (ASR)
- Bahasa Indonesia
- Character Error Rate (CER)
- Fisher Adam (FAdam)
- Wav2Vec2
- Word Error Rate (WER)
How to Cite
Irfan Darmawan, Alam Rahmatulloh, Rohmat Gunawan, R. Wahjoe Witjaksono, and Ghatan Fauzi Nugraha, "Indo-ASR+: Enhancing Indonesian Automatic Speech Recognition by Fine-Tuning Wav2Vec2 with FAdam," Journal of Advances in Information Technology, vol. 16, no. 10, pp. 1470-1478, 2025. https://doi.org/10.12720/jait.16.10.1470-1478
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
