# Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights

arXiv: https://arxiv.org/abs/2106.05852 · HTML: https://mlsanity.com/paper/2106.05852/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit](https://mlsanity.com/paper/1809.01446/) (2018)

## Built on by (most cited first, 14 of 14)

- [IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languages](https://mlsanity.com/paper/2403.01926/) (2024)
- [IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages](https://mlsanity.com/paper/2208.11761/) (2022)
- [Advocating Character Error Rate for Multilingual ASR Evaluation](https://mlsanity.com/paper/2410.07400/) (2024)
- [Vistaar: Diverse Benchmarks and Training Sets for Indian Language ASR](https://mlsanity.com/paper/2305.15386/) (2023)
- [A Benchmark and Dataset for Post-OCR text correction in Sanskrit](https://mlsanity.com/paper/2211.07980/) (2022)
- [Vakyansh: ASR Toolkit for Low Resource Indic languages](https://mlsanity.com/paper/2203.16512/) (2022)
- [SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes](https://mlsanity.com/paper/2302.09527/) (2023)
- [Vedavani: A Benchmark Corpus for ASR on Vedic Sanskrit Poetry](https://mlsanity.com/paper/2506.00145/) (2025)
- [NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data](https://mlsanity.com/paper/2507.00534/) (2025)
- [Word Segmentation and Morphological Parsing for Sanskrit](https://mlsanity.com/paper/2201.12833/) (2022)
- [Pingala: Prosody-Aware Decoding for Sanskrit Poetry Generation](https://mlsanity.com/paper/2603.24413/) (2026)
- [Syllable Subword Tokens for Open Vocabulary Speech Recognition in Malayalam](https://mlsanity.com/paper/2301.06736/) (2023)
- [Automatic Speech Recognition for Sanskrit with Transfer Learning](https://mlsanity.com/paper/2501.10024/) (2025)
- [Task-Lens: Cross-Task Utility Based Speech Dataset Profiling for Low-Resource Indian Languages](https://mlsanity.com/paper/2602.23388/) (2026)

## Abstract (arXiv)

Automatic speech recognition (ASR) in Sanskrit is interesting, owing to the various linguistic peculiarities present in the language. The Sanskrit language is lexically productive, undergoes euphonic assimilation of phones at the word boundaries and exhibits variations in spelling conventions and in pronunciations. In this work, we propose the first large scale study of automatic speech recognition (ASR) in Sanskrit, with an emphasis on the impact of unit selection in Sanskrit ASR. In this work, we release a 78 hour ASR dataset for Sanskrit, which faithfully captures several of the linguistic characteristics expressed by the language. We investigate the role of different acoustic model and language model units in ASR systems for Sanskrit. We also propose a new modelling unit, inspired by the syllable level unit selection, that captures character sequences from one vowel in the word to the next vowel. We also highlight the importance of choosing graphemic representations for Sanskrit and show the impact of this choice on word error rates (WER). Finally, we extend these insights from Sanskrit ASR for building ASR systems in two other Indic languages, Gujarati and Telugu. For both these languages, our experimental results show that the use of phonetic based graphemic representations in ASR results in performance improvements as compared to ASR systems that use native scripts.