# Make Patient Consultation Warmer: A Clinical Application for Speech Emotion Recognition
## 4 Discussion and Conclusions  
The detection rate for this study proves that emotion recognition has reached maturity in facial recognition and can be a practical approach for the situation when doctors wear masks or are outside the range of a camera, detection, and recognition of changes in facial emotions become impossible. In this study, an SER model was developed to address this shortcoming. The SER model can be applied to complement a facial emotion recognition system for a comprehensive emotion recognition system.  
This study adopts SER to solve accents and pronunciation in a language that may lower recognition accuracy. A comprehensive mandarin language SER model was built for a multi-lingual environment. The eigenvalues from the first to the thirteenth dimensions of MFCCs were employed in conjunction with AI to enable a machine to learn the characteristics of each emotion and automatically identify it, eliminating the need for manual emotion labeling. The sentiment recognition system developed by the hospital is used as a comparison answer to compare 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 was 86.3%.  
With the evolution of medical AI, studies have been conducted in developing various symptom assessment systems, and natural language processing has been performed in medical history research. However, to improve patient-doctor relationships, technological application in medical AI empathy is expected. This study aimed to identify speaker emotions through SER. In the future, by combining SER technology with facial emotion recognition, emotion recognition accuracy can be improved, thereby achieving AI empathy.  
Doctors should provide a comfortable and compassionate medical environment when treating patients and apply AI that is sensitive to interpersonal interactions. Patient confidence and persistence play a critical role in successful disease prevention, diagnosis, treatment, and management, and medical personnel behavior critically influences patient feelings. AI empathy should be implemented to assist medical personnel to correctly identify patient emotions and deliver appropriate emotional responses, thereby attaining personalized processing of patient-doctor relationships.  
Conceptualization, H.-C.L.; methodology, H.-C.L., T.P. and M.-H.L.; software, M.-H.L.; validation, T.P.; formal analysis, H.-C.L., T.P. and M.-H.L.; investigation, T.P. and M.-H.L.; resources, H.-C.L. and H.-W.C.; data curation, M.-H.L. and H.-W.C.; writing--original draft preparation, H.-C.L. and M.-H.L.; writing--review and editing, H.-C.L., T.P. and H.-W.C.; project administration, H.-C.L. and T.P.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
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