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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM33075

Emotion Detector and Solution Provider Using Voice Model

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM33075

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM33075

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

OS

Onkar Shinde

MD

Manisha Drave

VC

Vijaya Chickankar

P

Prof.Dr.S.M.Kulkarni

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Abstract

Emotion detection from images is one of the most high-powered tasks in today’s world and AI detection plays a fundamental role, this project aims to understand human emotions which play a major role in well-being, this project aims to dive deep into human emotions and provide a variety of solutions, through Human-computer interaction takes place, emotion detection and voice signals are input signal The project is classified into two major components: one where a human emo- tion is detected through camera is detected and facial recognition takes place. Secondly, where a solution is provided to a recorded emotion using a voice model, it creates a conversation which result turn provides a solution based on the collected database. The paper presents a theory-based framework of technologies discusses challenges and behavioural considerations, and gives directions for future practices and use.

KEYWORDS

Voice GPT Artificial intelligence (AI) Facial emotion recognition (FER) Emotion Detection (ED) Open CV Library (OCL) Convolutional neural networks (CNN) Machine Learning (ML) Tensor flow GTTP Keras.

Cite This Article

Onkar Shinde, Manisha Drave, Vijaya Chickankar, Prof.Dr.S.M.Kulkarni, "Emotion Detector and Solution Provider Using Voice Model," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM33075

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Publication Date

12/31/45420

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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