Enabling ASR for Low-Resource Languages: A Comprehensive Dataset Creation Approach
<|reference_start|>Enabling ASR for Low-Resource Languages: A Comprehensive Dataset Creation Approach: In recent years, automatic speech recognition (ASR) systems have significantly improved, especially in languages with a vast amount of transcribed speech data. However, ASR systems tend to perform poorly for low-resource languages with fewer resources, such as minority and regional languages. This study introduces a novel pipeline designed to generate ASR training datasets from audiobooks, which typically feature a single transcript associated with hours-long audios. The common structure of these audiobooks poses a unique challenge due to the extensive length of audio segments, whereas optimal ASR training requires segments ranging from 4 to 15 seconds. To address this, we propose a method for effectively aligning audio with its corresponding text and segmenting it into lengths suitable for ASR training. Our approach simplifies data preparation for ASR systems in low-resource languages and demonstrates its application through a case study involving the Armenian language. Our method, which is "portable" to many low-resource languages, not only mitigates the issue of data scarcity but also enhances the performance of ASR models for underrepresented languages.<|reference_end|>
@article{yeroyan2024enabling,
    title={Enabling ASR for Low-Resource Languages: A Comprehensive Dataset
  Creation Approach},
    author={Ara Yeroyan (Data Science Department, American University of Armenia),
  Nikolay Karpov (Nvidia, NeMo Conversational AI team)},
    journal={arXiv preprint arXiv:2406.01446},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01446},
    primaryClass={cs.CL cs.LG eess.AS eess.SP}
}