International Journal of Innovative Research in Computer Science and Technology
Year: 2026, Volume: 14, Issue: 1
First page : ( 79) Last page : ( 88)
Online ISSN : 2347-5552
Supritha P O
, Omkar Mahale
, Shalya Gaonkar
, Shetty Aditya Udaya
, Sooraj Devadiga
DOI: 10.55524/ijircst.2026.14.1.10 |
DOI URL: https://doi.org/10.55524/ijircst.2026.14.1.10
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)http://creativecommons.org/licenses/by/4.0
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Supritha P O , Omkar Mahale, Shalya Gaonkar, Shetty Aditya Udaya, Sooraj Devadiga
Pronunciation accuracy is a fundamental factor in effective language learning; however, many existing systems face difficulties in delivering real-time error analysis without relying on computationally intensive acoustic model training. This paper introduces an AI-driven pronunciation mistake detection system developed using Google Gemini 1.5 Flash, a low-latency multimodal large language model capable of directly processing spoken input. Unlike conventional approaches based on MFCC features or task-specific deep learning pipelines, the proposed system employs prompt-guided reasoning combined with algorithmic scoring methods to detect pronunciation errors at the word, phoneme, and prosodic levels. Learner speech is transmitted to the Gemini API, which generates a structured pronunciation analysis that includes phoneme-level interpretations and word-level discrepancies. These outputs are further processed by a custom scoring framework to evaluate pronunciation quality and produce clear, actionable feedback. Experimental evaluation using diverse English utterances demonstrates the system’s effectiveness in identifying vowel–consonant substitutions, omitted syllables, and stress-related errors. The findings underscore the potential of LLM-based audio reasoning as a lightweight, scalable, and real-time solution for automated pronunciation assessment.
Assistant Professor, CSE, SDMIT, Ujire, India
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