Vietnamese Automatic Speech Recognition: A Revisit
This addresses the challenge of building robust ASR systems for low-resource languages like Vietnamese, though it is incremental as it focuses on dataset improvement rather than a new model.
The authors tackled the problem of low-quality and inconsistent datasets for low-resource languages in automatic speech recognition by developing a data aggregation and preprocessing pipeline, resulting in a unified 500-hour Vietnamese dataset.
Automatic Speech Recognition (ASR) performance is heavily dependent on the availability of large-scale, high-quality datasets. For low-resource languages, existing open-source ASR datasets often suffer from insufficient quality and inconsistent annotation, hindering the development of robust models. To address these challenges, we propose a novel and generalizable data aggregation and preprocessing pipeline designed to construct high-quality ASR datasets from diverse, potentially noisy, open-source sources. Our pipeline incorporates rigorous processing steps to ensure data diversity, balance, and the inclusion of crucial features like word-level timestamps. We demonstrate the effectiveness of our methodology by applying it to Vietnamese, resulting in a unified, high-quality 500-hour dataset that provides a foundation for training and evaluating state-of-the-art Vietnamese ASR systems. Our project page is available at https://github.com/qualcomm-ai-research/PhoASR.