mlsanityData to 13 Oct 2026

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

arXiv 2506.02166 ↗First posted 2 Jun 2025eess.AS cs.AI cs.LGcited by 0

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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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 7 of its 28 references that are in the map. First posted on arXiv 2 Jun 2025.

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References · 7

  1. 2021Multilingual and code-switching ASR challenges for low resource Indian languagescited by 50
  2. 2021Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speechcited by 14
  3. 2021Mispronunciation Detection in Non-native (L2) English with Uncertainty Modelingcited by 5
  4. 2021Weakly-supervised word-level pronunciation error detection in non-native English speechcited by 4
  5. 2022Computer-assisted Pronunciation Training -- Speech synthesis is almost all you needcited by 4
  6. 2017Structural Analysis of Hindi Phonetics and A Method for Extraction of Phonetically Rich Sentences from a Very Large Hindi Text Corpuscited by 2
  7. 2022Improved Mispronunciation detection system using a hybrid CTC-ATT based approach for L2 English speakerscited by 2

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