Continued Pretraining for Low-Resource Swahili ASR: Achieving State-of-the-Art Performance with Minimal Labeled Data
Abstract
We investigate continued pretraining (CPT) for adapting wav2vec2-bert-2.0 to Swahili automatic speech recognition (ASR). Our approach combines unlabeled audio with limited labeled data through pseudo-labeled CPT followed by supervised finetuning. With 20,000 labeled samples, we achieve 3.24% WER on Common Voice Swahili-an 82% relative improvement over the baseline. This result surpasses the best previously reported academic system (8.3% WER from XLS-R) by 61% relative improvement. We provide concrete data requirements and a replicable methodology applicable to other low-resource languages.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a systematic evaluation of continued pretraining for Swahili ASR, achieving state-of-the-art performance with minimal labeled data. This work is significant in demonstrating the potential of leveraging unlabeled audio in low-resource language settings, providing a replicable methodology that could be applied to other underserved languages.
Methodology
The methodology presented in the paper is robust and systematic, focusing on the application of continued pretraining (CPT) for low-resource Swahili ASR. The authors clearly outline their three-stage pipeline, which includes training a labeling model, generating pseudo-labels, and performing supervised finetuning. This structured approach is well-justified and effectively demonstrates the potential of CPT in leveraging unlabeled data. The use of a strong baseline model for pseudo-labeling is particularly commendable, as it ensures the quality of the training data.
Results & evaluation
The experimental evaluation is thorough, with clear comparisons between models trained with and without CPT. The authors provide detailed results across different configurations, showcasing significant improvements in word error rate (WER). The benchmarks established are concrete and relevant, demonstrating a clear advancement over previous systems. The results are well-supported by the experimental design, which isolates the effects of CPT.
Reproducibility
The paper provides sufficient details regarding the experimental setup, including hyperparameters, dataset descriptions, and training procedures. However, the lack of a publicly accessible code repository or demo limits the reproducibility of the results. While the methodology is replicable, the absence of shared resources may hinder broader adoption.
Limitations
One limitation is the reliance on the quality of the pseudo-labels generated by the baseline model. If the labeling model does not perform well, it could adversely affect the continued pretraining process. Additionally, while the study focuses on Swahili, the generalizability of the findings to other low-resource languages remains to be validated.
Broader impact
The implications of this research are significant, particularly for the over 100 million Swahili speakers who could benefit from improved ASR technology. The methodology could pave the way for advancements in educational technology, accessibility tools, and the preservation of oral traditions in various African languages. The findings underscore the feasibility of developing high-quality ASR systems in low-resource settings, which could have a transformative effect on technology access in these communities. The paper presents a systematic evaluation of continued pretraining for Swahili ASR, achieving state-of-the-art performance with minimal labeled data. This work is significant in demonstrating the potential of leveraging unlabeled audio in low-resource language settings, providing a replicable methodology that could be applied to other underserved languages.
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