  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.
