부산대학교 도서관

The study of emotions has become a significant field of study that offers potential insights for a range of applications. Man-machine interfaces must be able to detect the user's emotional state and respond appropriately. Real-time applications of automatic emotion recognition include detecting the emotions of mobile phone users, call center operations, car drivers, pilots, etc. Emotions like happiness, joy, anger, sadness, neutral, boredom, disgust, fear and surprise can be predicted with the use of conventional algorithms such as the LSTM, Bayesian network using the Maximum Likelihood Estimation Principle and Support Vector Machine is proposed. MFCC (Mel-Frequency Cepstral Coefficients), Hamming/Hanning windows, Mel-Filter bank model were used in the proposed novel LSTM model. Implementation of novel LSTM model was performed and yielded an accuracy of 98(%). A dynamic webpage that can capture an individual's voice and predict their emotional state was implemented using the same.