Adaptive Artificial Neural Network Based Marathi Speech Database Emotion Recognition

2019 ◽  
pp. 59-67
Author(s):  
Lalita Anil Palange ◽  
Raviraj Vishwambhar Darekar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Emotion Recognition ◽  
Speech Database ◽  
Artificial Neural

scholarly journals Make Patient Consultation Warmer: A Clinical Application for Speech Emotion Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 4782
Author(s):  
Huan-Chung Li ◽  
Telung Pan ◽  
Man-Hua Lee ◽  
Hung-Wen Chiu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Speech Emotion Recognition ◽  
Single Observation ◽  
Artificial Neural

In recent years, many types of research have continued to improve the environment of human speech and emotion recognition. As facial emotion recognition has gradually matured through speech recognition, the result of this study provided more accurate recognition of complex human emotional performance, and speech emotion identification will be derived from human subjective interpretation into the use of computers to automatically interpret the speaker’s emotional expression. Focused on use in medical care, which can be used to understand the current feelings of physicians and patients during a visit, and improve the medical treatment through the relationship between illness and interaction. By transforming the voice data into a single observation segment per second, the first to the thirteenth dimensions of the frequency cestrum coefficients are used as speech emotion recognition eigenvalue vectors. Vectors for the eigenvalue vectors are maximum, minimum, average, median, and standard deviation, and there are 65 eigenvalues in total for the construction of an artificial neural network. The sentiment recognition system developed by the hospital is used as a comparison between the sentiment recognition results of the artificial neural network classification, and then use the foregoing results for a comprehensive analysis to understand the interaction between the doctor and the patient. Using this experimental module, the emotion recognition rate is 93.34%, and the accuracy rate of facial emotion recognition results can be 86.3%.

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