<![CDATA[The identification of depression patterns from human voices is important because depression can interfere with activities, reduce interest in learning, and hinder socialisation. Depression is a significant problem today because there has been a global increase in the number of people suffering from it. The factors contributing to depression are numerous and complex, and can affect all groups, from children to the elderly. The purpose of this study was to identify depression patterns based on voice feature extraction. The feature extraction method used is Mel-Frequency Cepstral Coefficients (MFCC). The MFCC method is capable of extracting features that closely resemble the human auditory system. The dataset used is the EATD-Corpus, which contains 162 recordings of students from Tongji University in China. The results of the study show that depression and healthy patterns can be distinguished using MFCC parameters, namely 25 measurements per frame, 10 frame intervals, an alpha value of 0.97 as the pre-emphasis coefficient, a maximum of 40 Mel filterbank coefficients, and 12 cepstral coefficients. Classification thresholds can be obtained for two classes: healthy with thresholds < 53.00 and depressed ≥ 53.00 using the Self-Rating Depression Scale.]]>
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