MUMBAI, India, Sept. 7 -- Intellectual Property India has published a patent application (202621088795 A) filed by Dr. Atulkumar Bhikhabhai Unagar on July 21, 2026, for Artificial Intelligence (ai) Based Sanskrit Pronunciation Analysis And Real-Time Voice Correction System And Method Thereof..
Inventor includes Dr. Atulkumar Bhikhabhai Unagar.
The application for the patent was published on September 04, 2026, under issue no. 36/2026.
Abstract: ABSTRACT [505] Accurate pronunciation of Sanskrit, characterized by precise articulation of retroflex, aspirated, and nasalized phonemes, distinct vowel length distinctions, and prescribed prosodic accentuation known as svara, constitutes a foundational requirement for Vedic recitation, classical textual study, and liturgical practice; however, learners frequently lack access to qualified human instructors capable of providing real-time, phoneme-level pronunciation correction, and existing language-learning technologies, being predominantly designed for contemporary spoken languages, fail to model the distinctive phonetic, phonemic, and prosodic characteristics that differentiate Sanskrit pronunciation assessment from conventional speech evaluation tasks. [510] Existing computer-assisted pronunciation training systems exhibit critical limitations including reliance on generic automatic speech recognition models trained predominantly on contemporary vernacular languages lacking adequate phonetic coverage of Sanskrit-specific retroflex and aspirated consonant contrasts, absence of vowel-length and prosodic accent modeling essential to correct Vedic and classical recitation, inability to provide real-time, sub-phoneme-level corrective feedback during live recitation, and lack of adaptive difficulty progression suited to learners across varying proficiency levels, collectively rendering existing tools inadequate for rigorous Sanskrit pronunciation instruction. [515] The integration of deep learning based acoustic phoneme modeling, forced alignment techniques, prosodic accent detection, and real-time corrective feedback generation presents transformative opportunities for developing an automated Sanskrit pronunciation training capability that provides instructor-independent, phoneme- accurate, real-time voice correction accessible to learners regardless of geographic location or access to qualified human instruction. [520] The present invention describes an Artificial Intelligence Based Sanskrit Pronunciation Analysis and Real-Time Voice Correction System and Method comprising a Sanskrit-specific acoustic phoneme recognition module trained on a curated corpus of expert Sanskrit recitation encompassing retroflex, aspirated, and nasalized phoneme contrasts; a forced- alignment module that temporally maps learner speech input against a reference phoneme sequence derived from the target text; a prosodic accent and vowel-length analysis module that evaluates svara accentuation and vowel duration against prescribed classical norms; a real-time deviation scoring engine that computes phoneme- level and prosody-level pronunciation deviation scores; and a corrective feedback generation module that delivers immediate, articulation-specific corrective guidance to the learner during live recitation. [525] Comprehensive evaluation of the system across a benchmark corpus of Sanskrit learner recitations demonstrated phoneme- level pronunciation error detection accuracy of 93.4 ± 1.8 percent, prosodic accent classification accuracy of 88.7 ± 2.4 percent, mean corrective feedback latency of under 350 milliseconds following completion of the target phonemic unit, and learner pronunciation accuracy improvement of 27.3 ± 3.6 percentage points following a structured ten-session training regimen utilizing the system, compared to 9.8 ± 2.1 percentage points for a control group utilizing conventional audio-playback based self-study methods. [530] These findings confirm that the Artificial Intelligence Based Sanskrit Pronunciation Analysis and Real-Time Voice Correction System and Method constitutes a significant advancement in computer-assisted classical language pronunciation instruction, with deployment applicability spanning Vedic education institutions, Sanskrit language learning applications, liturgical and ritual training programmes, and academic Sanskrit curricula, thereby addressing the substantial unmet need for scalable, accurate, and accessible Sanskrit pronunciation instruction independent of qualified human instructor availability.
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