Depression Classification Algorithm Based on Voice Signals Using MFCC and CNN Autoencoders

Depression is a very common mental illness. In severe cases, it is a scary disease that can lead to suicide. Consequently, early diagnosis is essential because it can improve with appropriate treatment if discovered early. Recently, research on voice-based automatic depression detection systems has been actively conducted. Most existing studies to date have diagnosed depression by analyzing the characteristics of voice signals fragmented. Data is not used efficiently because the signal is analyzed only from a specific and fragmentary aspect. To solve this problem, we propose a method to extract features from both the time series and spatial aspects of the signal. First, MFCC (Mel-Frequency Cepstral Coefficient) is used to extract important features through time series analysis of speech signal. Then these extracted features are inputted into a CNN AE (Convolutional Auto-Encoder) to capture complex patterns among the initial features. This methodology can extract features that contain important information from both time series and spatial aspects. Consequently, it makes it possible to learn features and subtle signals highly associated with depression from speech data. The proposed method analyzes voice signals in both time series and spatial aspects, allowing the model to utilize more diverse information. As a result, depression can be diagnosed more accurately. To evaluate the performance of the proposed method, experiments are performed on the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) dataset and compare with frequently used feature extraction methods in voice signal analysis, such as MFCC and CNN. As a result, the proposed method showed an accuracy of 91%, an improvement of about 15% compared to previous studies.