# Dhvani: A Weakly-supervised Phonemic Error Detection and Personalized Feedback System for Hindi

arXiv: https://arxiv.org/abs/2506.02166 · HTML: https://mlsanity.com/paper/2506.02166/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [Multilingual and code-switching ASR challenges for low resource Indian languages](https://mlsanity.com/paper/2104.00235/) (2021)
- [Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech](https://mlsanity.com/paper/2106.12896/) (2021)
- [Mispronunciation Detection in Non-native (L2) English with Uncertainty Modeling](https://mlsanity.com/paper/2101.06396/) (2021)
- [Weakly-supervised word-level pronunciation error detection in non-native English speech](https://mlsanity.com/paper/2106.03494/) (2021)
- [Computer-assisted Pronunciation Training -- Speech synthesis is almost all you need](https://mlsanity.com/paper/2207.00774/) (2022)
- [Structural Analysis of Hindi Phonetics and A Method for Extraction of Phonetically Rich Sentences from a Very Large Hindi Text Corpus](https://mlsanity.com/paper/1701.08655/) (2017)
- [Improved Mispronunciation detection system using a hybrid CTC-ATT based approach for L2 English speakers](https://mlsanity.com/paper/2201.10198/) (2022)

## Abstract (arXiv)

Computer-Assisted Pronunciation Training (CAPT) has been extensively studied for English. However, there remains a critical gap in its application to Indian languages with a base of 1.5 billion speakers. Pronunciation tools tailored to Indian languages are strikingly lacking despite the fact that millions learn them every year. With over 600 million speakers and being the fourth most-spoken language worldwide, improving Hindi pronunciation is a vital first step toward addressing this gap. This paper proposes 1) Dhvani -- a novel CAPT system for Hindi, 2) synthetic speech generation for Hindi mispronunciations, and 3) a novel methodology for providing personalized feedback to learners. While the system often interacts with learners using Devanagari graphemes, its core analysis targets phonemic distinctions, leveraging Hindi's highly phonetic orthography to analyze mispronounced speech and provide targeted feedback.