Utilizing Machine Learning for Speech Emotion Recognition
Keywords:Speech Emotion Recognition, Machine Learning, MLP Classifier, Accuracy
Voice emotion recognition, a captivating field, employs machine learning techniques to identify and interpret emotions conveyed through speech. The primary objective of this research is to achieve accurate emotion recognition and classification by leveraging advanced algorithms and data analysis techniques. Throughout the process, significant features like pitch, intensity, and spectral characteristics are extracted from a vast collection of labeled voice recordings. Machine learning models including Support Vector Machines, Multilayer Perceptron (MLP) classifiers, Convolutional Neural Networks, and LSTM are then trained on this data to uncover patterns and correlations between these features and emotions. Once trained, these models can be employed to identify emotions in real-time speech inputs. The applications of speech emotion recognition span across multiple domains, encompassing virtual assistants, mental health monitoring, human-computer interaction, and entertainment. However, several challenges such as variability, subjectivity, cultural differences, and contextual influences must be addressed to enhance the accuracy and robustness of speech emotion recognition systems. Ongoing research endeavors seek to overcome these challenges and improve the performance of such systems. The integration of machine learning techniques into speech emotion recognition opens up exciting possibilities for comprehending and analyzing emotions in speech, contributing to a deeper understanding of human communication and interaction. Moreover, this technology holds practical implications in various fields.
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