Indonesian Automatic Speech Recognition with XLSR-53
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 27 references that are in the map. First posted on arXiv 20 Aug 2023.
Builds on
8 of 27 references.
References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
- 2020Unsupervised Cross-lingual Representation Learning for Speech Recognitioncited by 501
- 2019vq-wav2vec: Self-Supervised Learning of Discrete Speech Representationscited by 340
- 2019Effectiveness of self-supervised pre-training for speech recognitioncited by 72
- 2019Improving Transformer-based Speech Recognition Using Unsupervised Pre-trainingcited by 30
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Citing papers per year: 2024: 2.
2 papers.