Speech Emotion Recognition Using Neural Networks

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. 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, URL: https://www.ijtsrd.com/papers/ijtsrd47958.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/47958/speech-emotion-recognition-using-neural-networks/anirban-chakraborty