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Depression is a common mental disorder which has been affecting millions ofpeople around the world and becoming more severe with the arrival of COVID-19.Nevertheless proper diagnosis is not accessible in many regions due to a severeshortage of psychiatrists. This scarcity is worsened in low-income countrieswhich have a psychiatrist to population ratio 210 times lower than that ofcountries with better economies. This study aimed to explore applications ofdeep learning in diagnosing depression from voice samples. We collected datafrom the DAIC-WOZ database which contained 189 vocal recordings from 154individuals. Voice samples from a patient with a PHQ-8 score equal or higherthan 10 were deemed as depressed and those with a PHQ-8 score lower than 10were considered healthy. We applied mel-spectrogram to extract relevantfeatures from the audio. Three types of encoders were tested i.e. 1D CNN, 1DCNN-LSTM, and 1D CNN-GRU. After tuning hyperparameters systematically, we foundthat 1D CNN-GRU encoder with a kernel size of 5 and 15 seconds of recordingdata appeared to have the best performance with F1 score of 0.75, precision of0.64, and recall of 0.92.

Automatic Detection of Depression from Stratified Samples of Audio Data