A Comparative Analysis of Long Short-Term Memory and Bidirectional Long Short-Term Memory Models for Bugis Speech-to-Text Conversion

Abstrak

Regional languages play an important role in cultural preservation and strengthening ethnic identity. Bugis, one of the most widely spoken regional languages, can be studied through speech recognition technology. This study analyzes the performance of two Deep Learning models, Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM), for converting Bugis speech into text. The dataset consists of 2,500 audio recordings from native speakers along with their transcripts. The preprocessing stage includes trimming, denoising, and feature extraction using Mel-Frequency Cepstral Coefficients (MFCC). Evaluation using Word Error Rate (WER) and Bilingual Evaluation Understudy (BLEU) shows that BiLSTM outperforms LSTM, achieving a WER of 4.88% and a BLEU of 86.82%, compared to LSTM’s WER of 90.33% and BLEU of 3.86%. These findings indicate that BiLSTM is more effective in capturing contextual information, resulting in more accurate transcriptions. This research contributes to the preservation of the Bugis language and provides insights into the application of Deep Learning in low-resource speech recognition. Future work will explore attention mechanisms and larger datasets to further improve model performance

Identifier

• OpenAlex: https://openalex.org/W7130927017
• DOI: https://doi.org/10.31763/iota.v5i4.1043