TY - JOUR
T1 - Make patient consultation warmer
T2 - A clinical application for speech emotion recognition
AU - Li, Huan Chung
AU - Pan, Telung
AU - Lee, Man Hua
AU - Chiu, Hung Wen
N1 - Funding Information:
Acknowledgments: We want to thank that the project of Ministry of Science and Technology, Taiwan (MOST 108-2634-F-038-002) provided part of data for this research.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/6/1
Y1 - 2021/6/1
N2 - 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%.
AB - 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%.
KW - Doctor-patient communication
KW - Mel Frequency Cepstrum Coefficients
KW - Speech emotion recognition
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U2 - 10.3390/app11114782
DO - 10.3390/app11114782
M3 - Article
AN - SCOPUS:85107292731
SN - 2076-3417
VL - 11
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 11
M1 - 4782
ER -
