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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM68058

Speech Enhancer: Real Time Correction Using AI/ML

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68058

Volume:

10 |

10 |

Oct 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM68058

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.

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Authors

K Prasanth

K Prasanth

Computer Science and Engineering

East Point College of Engineering and Technology

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Divyashree N

Divyashree N

Computer Science and Engineering

East Point College of Engineering and Technology

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Abstract

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

Speech Enhancer Speech Recognition Pronunciation Analysis Stutter Detection NLP Real-time Feedback.

Cite This Article

K Prasanth, Divyashree N, "Speech Enhancer: Real Time Correction Using AI/ML," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-5, Oct 2026. https://doi.org/10.55041/IJSREM68058

Full Article/Conclusion

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.

References

[1] S. Ghosh, A. Tsiartas, and S. Narayanan, "Automatic Detection of Speech Dysfluencies Using Deep Neural Networks," IEEE Transactions on Audio, Speech, and Language Processing. [2] H. Ming, L. Dong, and F. Soong, "Acoustic Modeling for Pronunciation Scoring," in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [3] J. Kourkounakis, A. Hajavi, and A. Ghahremani, "Detecting Stuttering from Raw Speech Using Deep Learning," in Proc. IEEE Spoken Language Technology Workshop (SLT). [4] D. Jurafsky and J. H. Martin, Speech and Language Processing. Upper Saddle River, NJ, USA: Prentice Hall. [5] P. Boersma and D. Weenink, Praat: Doing Phonetics by Computer. Software for Phonetic and Pronunciation Analysis. [6] T. Drugman and M. Thomas, "Voice Activity Detection: A Deep Learning Approach Combining Neural Networks and Signal Processing Features," IEEE/ACM Transactions on Audio, Speech, and Language Processing. [7] A. Graves, A. Mohamed, and G. Hinton, "Speech Recognition with Deep Recurrent Neural Networks," in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [8] J. Kourkounakis, A. Hajavi, and A. Ghahremani, "Detecting Dysfluencies in Speech Using wav2vec 2.0," in Proc. IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). [9] D. Jurafsky and C. Manning, "Natural Language Processing for Grammar Correction and Language Learning," in Proc. Association for Computational Linguistics (ACL). [10] M. Yuan and X. Wang, "Automatic Pronunciation Assessment for Language Learning Using Deep Learning Models," International Journal of Artificial Intelligence in Education.

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Publication Date

10/17/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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