# VAANI: Capturing the language landscape for an inclusive digital India

arXiv: https://arxiv.org/abs/2603.28714 · HTML: https://mlsanity.com/paper/2603.28714/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2026.

## Built on by (most cited first, 5 of 5)

- [Connecting Speech to Words through Images](https://mlsanity.com/paper/2606.16807/) (2026)
- [Seeds Before Objectives: Rethinking Evaluation for Low-Resource Garhwali ASR](https://mlsanity.com/paper/2608.10670/) (2026)
- [Rethinking Indic AI from a Lens of Cultural Heritage Preservation](https://mlsanity.com/paper/2607.06544/) (2026)
- [FormBharo: Designing and Evaluating a Voice Agent for Conversational Form Filling in Rural India](https://mlsanity.com/paper/2608.06027/) (2026)
- [Mapping Written Words to Spoken Words in a Different Language Using Only Visual Grounding](https://mlsanity.com/paper/2608.26925/) (2026)

## Abstract (arXiv)

Voice based technologies have the potential to bridge digital accessibility gaps; however, existing datasets fail to capture the linguistic and regional diversity of Indic languages. We present Project VAANI, a large scale multimodal dataset designed to represent India's linguistic landscape across 165 districts. Speech data is collected using image based prompts to elicit spontaneous responses, while images are curated through a separate pipeline covering diverse themes across regions. The dataset undergoes a rigorous multi stage quality control process, combining automated and manual evaluation to ensure high audio quality and transcription accuracy. We release approximately 289K images, 31,255 hours of speech, and 2,043 hours of transcribed audio spanning 105 languages from 28 states and 3 union territories. Many of these languages are represented at this scale for the first time, making VAANI a foundational resource for inclusive speech technology. The dataset enables the development of robust, multilingual, and multimodal models, and supports research in speech recognition, language understanding, and cross-modal learning for underrepresented languages.