Open AccessSpeech Emotion Recognition System
Author(s) -
Sourabh Suke,
Ganesh Regulwar,
Nikesh Aote,
Pratik Chaudhari,
Rajat Ghatode,
Mahima Pimple,
Vishakha Bijekar
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-v4-i3-024
Subject(s) - speech recognition , mel frequency cepstrum , disgust , sadness , computer science , surprise , anger , set (abstract data type) , emotion classification , convolutional neural network , support vector machine , human voice , cepstrum , multilayer perceptron , artificial intelligence , feature extraction , artificial neural network , psychology , communication , psychiatry , programming language
This project describes "VoiEmo- A Speech Emotion Recognizer", a system for recognizing the emotional state of an individual from his/her speech. For example, one's speech becomes loud and fast, with a higher and wider range in pitch, when in a state of fear, anger, or joy whereas human voice is generally slow and low pitched in sadness and tiredness. We have particularly developed a classification model speech emotion detection based on Convolutional neural networks (CNNs), Support Vector Machine (SVM), Multilayer Perceptron (MLP) Classification which make predictions considering the acoustic features of speech signal such as Mel Frequency Cepstral Coefficient (MFCC). Our models have been trained to recognize seven common emotions (neutral, calm, happy, sad, angry, fearful, disgust, surprise). For training and testing the model, we have used relevant data from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset and the Toronto Emotional Speech Set (TESS) Dataset. The system is advantageous as it can provide a general idea about the emotional state of the individual based on the acoustic features of the speech irrespective of the language the speaker speaks in, moreover, it also saves time and effort. Speech emotion recognition systems have their applications in various fields like in call centers and BPOs, criminal investigation, psychiatric therapy, the automobile industry, etc.