# Make Patient Consultation Warmer: A Clinical Application for Speech Emotion Recognition
###### Abstract  
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 cepstrum 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%. doctor-patient communication; speech emotion recognition; Mel Frequency Cepstrum Coefficients +
Footnote †: journal: Medical  
interactions [6], emotion integration in robots [7], computer games [8], and psychological assessment [9]. Although emotion recognition has been adopted in a broad way to daily living, emotions involve subjective perceptions making the identification of human emotions a challenge. The performance could not be improved beyond a certain limit as hand-crafted features are not very capable of capturing complex phenomena such as emotional state [10]. Over the past decade, deep learning techniques have been employed in several fields of research such as image classification, speaker recognition, speech and handwriting recognition, etc. because of their ability to capture high-level discriminative features which are otherwise not captured well in traditional handcrafted features. SER requires the use of a classifier, which usually employs a supervised learning algorithm in training a module to identify the emotion of a new voice signal. Supervised learning is crucial in emotion labeling. Voice signals must be preprocessed to highlight their features for extraction. These features consist of prosodies, frequency, and voice quality. In recent studies, AI in data analysis and classifiers integrated with deep learning algorithms have been prevalent [10; 11; 12]. Frequency features are frequently examined as eigenvalues in voice recognition and have also been jointly investigated with prosodies in experiments. Human voices are filtered by sound channels when they are generated and, after filtering, the resulting voices are defined by their shapes. Any voice exhibits notable frequency features [13]. Frequency features are acquired by converting time-domain signals into frequency-domain signals through Fourier transform. Specifically, a speech is divided into blocks, and each block is converted to frequency-domain signals through discrete short-time Fourier transform. A mel-scale filter bank is applied to calculate the energy of sub-bands. The logarithms of the sub-bands are then calculated. Finally, inverse Fourier transform is conducted to acquire mel-frequency cepstrum coefficients (MFCCs), which are the most widely used frequency features [14]. Numerous studies on MFCCs have involved comparing prosodic features with spectral features and have shown that spectral features exhibit remarkable results [15].