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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68058
Volume:
10 |
10 |
Oct 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Computer Science and Engineering
East Point College of Engineering and Technology
Computer Science and Engineering
East Point College of Engineering and Technology
The growing importance of effective communication in academic, professional, and personal contexts highlights the necessity of tools that support structured and continuous speech improvement. However, traditional learning environments often lack real-time feedback, personalized guidance, and objective evaluation of spoken performance. This idea addresses these setbacks by developing an AI-powered Speech Enhancer system designed to analyze and improve pronunciation, grammar, and speech fluency through real-time interaction. The system integrates advanced speech recognition, acoustic analysis, stutter detection, and NLP-based grammar evaluation to provide users with immediate, actionable feedback. A modular architecture built with modern backend technologies ensures smooth audio processing, while the user interface delivers clear visual guidance and session-wise progress tracking. Procedural evaluation brings high accuracy in detecting stuttering patterns, strong alignment between AI-based pronunciation scoring and reliable grammar correction performance. Overall, the Speech Enhancer system enhances spoken communication by offering a structured, intelligent, and interactive platform that supports continuous speech practice and measurable improvement.
KEYWORDS
The Speech Enhancer successfully delivers an AI-powered, real-time platform for correcting mispronunciation, stuttering, and grammatical errors in spontaneous speech. By integrating ASR, acoustic-phonemic analysis, NLP, and dysfluency detection within a unified modular architecture, the system achieves an end-to-end response latency of approximately 400 ms — satisfying the real-time feedback requirement. All three core modules — grammar correction, pronunciation assessment, and stuttering detection — were validated through structured testing, confirming functional accuracy, responsiveness, and usability. The system's combination of immediate feedback, contextual explanations, and longitudinal progress tracking establishes a complete improvement cycle, positioning it as an effective tool for individuals in clinical, academic, and professional speech development contexts.
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Publication Date
10/17/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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