End-to-End Speech Translation for Low-Resource Languages Using Weakly Labeled Data
This addresses the data scarcity problem for low-resource language translation, though it is incremental as it builds on existing methods for weakly labeled data.
The paper tackled the challenge of building end-to-end speech-to-text translation systems for low-resource languages by using weakly labeled data, achieving performance comparable to massive multi-modal multilingual baselines like SONAR and SeamlessM4T.
The scarcity of high-quality annotated data presents a significant challenge in developing effective end-to-end speech-to-text translation (ST) systems, particularly for low-resource languages. This paper explores the hypothesis that weakly labeled data can be used to build ST models for low-resource language pairs. We constructed speech-to-text translation datasets with the help of bitext mining using state-of-the-art sentence encoders. We mined the multilingual Shrutilipi corpus to build Shrutilipi-anuvaad, a dataset comprising ST data for language pairs Bengali-Hindi, Malayalam-Hindi, Odia-Hindi, and Telugu-Hindi. We created multiple versions of training data with varying degrees of quality and quantity to investigate the effect of quality versus quantity of weakly labeled data on ST model performance. Results demonstrate that ST systems can be built using weakly labeled data, with performance comparable to massive multi-modal multilingual baselines such as SONAR and SeamlessM4T.