Time-frequency Analysis Based on Hilbert-Huang Transform for Depression Recognition in Speech

In recent years, automatic detection of depression from speech has attracted many researchers. One of the key points is finding discriminable patterns in voice between depressed patients and healthy people. For this goal, we employed the Hilbert-Huang transform (HHT) to implement time-frequency analysis. Speech signals were decomposed into different sub-band signals and further were transformed into energy-frequency features for analysis and detection of depression. In the experiment 124 participants' (68 females and 56 males) speech were recorded in three patterns: interview, reading, and picture description for data collection. The results showed that the energy distribution of intrinsic mode functions (IMFs) between depressed patients and healthy people was significantly different, and this difference mainly was found in a relatively high-frequency range (1kHz). This finding fitted the clinical observation of depressed patients' energy loss. Further, a speech-based depression classification model based on the above finding was built and validated on the dataset. The results showed classification accuracy was 75.5% and 71.2% for female and male, respectively and each specificity was 88.4% and 78.2% These results implied HHT-based energy-frequency feature is a promising indicator for automatic depression assessment.