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Speech Emotion Recognition Using Neural Networks

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Speech Emotion Recognition Using Neural Networks

Anirban Chakraborty

Anirban Chakraborty "Speech Emotion Recognition Using Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1, December 2021, pp.922-927, URL: https://www.ijtsrd.com/papers/ijtsrd47958.pdf

Speech is the most natural and easy method for people to communicate, and interpreting speech is one of the most sophisticated tasks that the human brain conducts. The goal of Speech Emotion Recognition (SER) is to identify human emotion from speech. This is due to the fact that tone and pitch of the voice frequently reflect underlying emotions. Librosa was used to analyse audio and music, sound file was used to read and write sampled sound file formats, and sklearn was used to create the model. The current study looked on the effectiveness of Convolutional Neural Networks (CNN) in recognising spoken emotions. The networks' input characteristics are spectrograms of voice samples. Mel-Frequency Cepstral Coefficients (MFCC) are used to extract characteristics from audio. Our own voice dataset is utilised to train and test our algorithms. The emotions of the speech (happy, sad, angry, neutral, shocked, disgusted) will be determined based on the evaluation.

Speech emotion, Energy, Pitch, Librosa, Sklearn, Sound file, CNN, Spectrogram, MFCC

Volume-6 | Issue-1, December 2021
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. 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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