Methods to Increase the Amount of Data for Speech Recognition for Low Resource Languages

TL;DR

This paper investigates cost-effective methods to augment data for low-resource speech recognition, demonstrating that paid crowdsourcing yields the best results and providing improved models for Armenian and Georgian languages.

Contribution

It provides practical strategies for dataset expansion in low-resource languages, highlighting the effectiveness of paid crowdsourcing and open-sourcing models for Armenian and Georgian.

Findings

Paid crowdsourcing outperforms other data collection methods.

Expanded datasets improve ASR performance significantly.

Open-sourced models facilitate further research.

Abstract

This study explores methods to increase data volume for low-resource languages using techniques such as crowdsourcing, pseudo-labeling, advanced data preprocessing and various permissive data sources such as audiobooks, Common Voice, YouTube. While these methods are well-explored for highresource languages, their application for low-resource languages remains underexplored. Using Armenian and Georgian as case studies, we demonstrate how linguistic and resource-specific characteristics influence the success of these methods. This work provides practical guidance for researchers to choose cost-effective and quality-driven dataset extension strategies for low-resource languages. The key takeaway from various data extension approaches is that paid crowd-sourcing offers the best balance between cost and quality, outperforming volunteer crowd-sourcing, open-source audiobooks, and unlabeled data usage. Ablation study shows that models trained on the expanded datasets outperform existing baselines and achieve 5.73% for Gergian and 9.9% for Armenian ASR word error rate using a relatively small FastConformer architecture. We open-sourced both the Armenian and Georgian models to allow further research and practical applications.