Dhvani: A Weakly-supervised Phonemic Error Detection and Personalized Feedback System for Hindi
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915320166350848 |
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| author | Rustagi, Arnav Bajpai, Satvik Kaur, Nimrat Siddharth, Siddharth |
| author_facet | Rustagi, Arnav Bajpai, Satvik Kaur, Nimrat Siddharth, Siddharth |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02166 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dhvani: A Weakly-supervised Phonemic Error Detection and Personalized Feedback System for Hindi Rustagi, Arnav Bajpai, Satvik Kaur, Nimrat Siddharth, Siddharth Audio and Speech Processing Artificial Intelligence Machine Learning 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. |
| title | Dhvani: A Weakly-supervised Phonemic Error Detection and Personalized Feedback System for Hindi |
| topic | Audio and Speech Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.02166 |