Same-language subtitles enhance consumers’ experience for audiovisual content for both hearing impaired population. However, while high-resource languages can benefit from automatic subtitling, subtitles are seldom available for content creators in regional languages. This limits audience engagement on their content, which often is independently produced. This paper presents Project Saurakhi, a platform for generating same-language subtitles in regional languages. To achieve this, we first extract community-generated YouTube videos serve as the primary data source for this project. The current dataset comprises 63 hours of Bundelkhandi speech sourced from 207 YouTube videos across 19 content creators. And second, the technical workflow integrates automated stages with manual refinement via a mobile annotation platform. As regional language content grows both in independent productions, and in over-the-top platforms, Project Saurakhi aims to train women participants in rural India to become proficient in providing subtitles in their native languages. corpus creation, low-resource languages, Bundelkhandi, Indian languages, conversational AI, speech recognition, YouTube data

We present a culturally-grounded multimodal dataset of 1,060 traditional recipes crowdsourced from rural communities across remote regions of Eastern India, spanning 10 endangered languages. These recipes, rich in linguistic and cultural nuance, were collected using a mobile interface designed for contributors with low digital literacy. Endangered Language Recipes (ELR)-1000—captures not only culinary practices but also the socio-cultural context embedded in indigenous food traditions. We evaluate the performance of several state-of-the-art large language models (LLMs) on translating these recipes into English and find the following: despite the models’ capabilities, they struggle with low-resource, culturally-specific language. However, we observe that providing targeted context—including background information about the languages, translation examples, and guidelines for cultural preservation—leads to significant improvements in translation quality. Our results underscore the need for benchmarks that cater to underrepresented languages and domains to advance equitable and culturally-aware language technologies. As part of this work, we release the ELR-1000 dataset to the NLP community, hoping it motivates the development of language technologies for endangered languages.