Speech-to-Speech Translation (S2ST) focuses on generating spoken output in a target language directly from spoken input in a source language. Despite progress in S2ST modeling, low-resource Indic languages remain poorly supported, primarily because large-scale parallel speech corpora are unavailable. We present UrHiOdSynth, a three-language parallel S2ST dataset containing approximately 75 hours of speech across Urdu, Hindi, and Odia. The corpus consists of 10,735 aligned sentence triplets, with an average utterance length of 8.45 seconds. To our knowledge, UrHiOdSynth represents the largest multi-domain resource offering aligned speech and text for S2ST in this language context. Beyond speech-to-speech translation, the dataset supports tasks such as automatic speech recognition, speech-to-text translation, text-to-speech synthesis, and machine translation. This flexibility enables the training of unified multilingual models, particularly for low-resource Indic languages.

Maithili is one of the 22 official languages recognized in the Indian Constitution. The literature of Maithili is rich; however, due to current socio-political changes, the language is on the verge of extinction. Therefore, it is crucial to develop a corpus for low-resource Indic languages like Maithili to ensure that the dream of “No Language Left Behind" (NLLB) is realized. With this in mind, we contribute a corpus (1,05,600 sentences) containing both manually curated and synthetically generated. Additionally, we propose a strong baseline on the Maithali-Hindi pair using multilingual pretrained models such as IndicTrans2, mBART50, mT5, and NLLB-200 distilled. We evaluate the translation systems using standard performance metrics, including BLEU, CHRF2, TER, COMET, METEOR, and BERTScore. Comparative experiments conducted against the existing NLLB dataset (5,50,300 sentence pairs) demonstrate that our proposed dataset consistently yields superior translation quality. Finally, these results demonstrate that, even with a smaller corpus size, high-quality, task-specific data significantly enhance translation accuracy for low-resource Indian languages, such as Maithili.