Page No: 65-77
Abstract : In modern days, person-computer communication systems have gradually penetrated
our lives. One of the crucial technologies in person-computer communication systems, Speech
Emotion Recognition (SER) technology, permits machines to correctly recognize emotions and
greater understand users' intent and human-computer interlinkage. The main objective of the
SER is to improve the human-machine interface. It is also used to observe a person's
psychological condition by lie detectors. Automatic Speech Emotion Recognition(SER) is vital in
the person-computer interface, but SER has challenges for accurate recognition. In this work to
resolve the above problem, automatic Speech enhancement shows that deep learning
techniques effectively eliminate background noise. Using Deep leaning models for four states
were created: happy, sad, angry, and intoxicated. Recurrent Neural Network (RNN) algorithm
used to reduce the possibility of over fitting by randomly omitting neurons in the hidden layers.
The proposed RNN method could be implemented in personal assistant systems to give better
and more appropriate state-based interactions between humans. In the simulation results shows
Improving accuracy, Time complexity, Error rate is also reduced to using the proposed method.
Keyword Speech Emotion Recognition (SER), Speech emotion detection, deep leaning,
Recurrent Neural Network (RNN), preprocessing, feature extraction.