Indonesian Automatic Speech Recognition with XLSR-53

TL;DR

This paper demonstrates that using the XLSR-53 pre-trained model enables effective Indonesian speech recognition with limited data, achieving competitive accuracy and improved WER through language model integration.

Contribution

The study introduces an Indonesian ASR system utilizing XLSR-53, reducing data requirements and enhancing performance compared to previous methods.

Findings

Achieved 20% WER with 24 hours of data.

Reduced WER to 12% with a language model.

Outperformed previous Indonesian ASR models.

Abstract

This study focuses on the development of Indonesian Automatic Speech Recognition (ASR) using the XLSR-53 pre-trained model, the XLSR stands for cross-lingual speech representations. The use of this XLSR-53 pre-trained model is to significantly reduce the amount of training data in non-English languages required to achieve a competitive Word Error Rate (WER). The total amount of data used in this study is 24 hours, 18 minutes, and 1 second: (1) TITML-IDN 14 hours and 31 minutes; (2) Magic Data 3 hours and 33 minutes; and (3) Common Voice 6 hours, 14 minutes, and 1 second. With a WER of 20%, the model built in this study can compete with similar models using the Common Voice dataset split test. WER can be decreased by around 8% using a language model, resulted in WER from 20% to 12%. Thus, the results of this study have succeeded in perfecting previous research in contributing to the creation of a better Indonesian ASR with a smaller amount of data.