Enabling ASR for Low-Resource Languages: A Comprehensive Dataset Creation Approach
Abstract
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.
Cite
@article{arxiv.2406.01446,
title = {Enabling ASR for Low-Resource Languages: A Comprehensive Dataset Creation Approach},
author = {Ara Yeroyan and Nikolay Karpov},
journal= {arXiv preprint arXiv:2406.01446},
year = {2024}
}
Comments
13 pages, 10 figures (including ablation studies), to be published in 2024 IEEE Spoken Language Technology Workshop. Additionally, the associated software package can be accessed at (https://pypi.org/project/vac-aligner/) for practical applications and further development