Enabling ASR for Low-Resource Languages: A Comprehensive Dataset Creation Approach
This addresses data scarcity for ASR in low-resource languages like minority and regional ones, though it is incremental as it adapts existing alignment and segmentation techniques.
The study tackled the problem of poor ASR performance in low-resource languages by developing a pipeline to generate training datasets from audiobooks, demonstrating its application with Armenian and showing it mitigates data scarcity and enhances model performance.
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.