The applicability of the Beck Depression Inventory and Hamilton Depression Scale in the automatic recognition of depression based on speech signal processing.

Depression is a growing problem worldwide, impacting on an increasing number of patients, and also affecting health systems and the global economy. The most common diagnostical rating scales of depression are self-reported or clinician-administered, which differ in the symptoms that they are sampling. Speech is a promising biomarker in the diagnostical assessment of depression, due to non-invasiveness and cost and time efficiency. In our study, we try to achieve a more accurate, sensitive model for determining depression based on speech processing. Regression and classification models were also developed using a machine learning method. During the research, we had access to a large speech database that includes speech samples from depressed and healthy subjects. The database contains the Beck Depression Inventory (BDI) score of each subject and the Hamilton Rating Scale for Depression (HAMD) score of 20% of the subjects. This fact provided an opportunity to compare the usefulness of BDI and HAMD for training models of automatic recognition of depression based on speech signal processing. We found that the estimated values of the acoustic model trained on BDI scores are closer to HAMD assessment than to the BDI scores, and the partial application of HAMD scores instead of BDI scores in training improves the accuracy of automatic recognition of depression.

FMRI repetition suppression for voices is modulated by stimulus expectations.

According to predictive coding models of sensory processing, stimulus expectations have a profound effect on sensory cortical responses. This was supported by experimental results, showing that fMRI repetition suppression (fMRI RS) for face stimuli is strongly modulated by the probability of stimulus repetitions throughout the visual cortical processing hierarchy. To test whether processing of voices is also affected by stimulus expectations, here we investigated the effect of repetition probability on fMRI RS in voice-selective cortical areas. Changing ('alt') and identical ('rep') voice stimulus pairs were presented to the listeners in blocks, with a varying probability of alt and rep trials across blocks. We found auditory fMRI RS in the nonprimary voice-selective cortical regions, including the bilateral posterior STS, the right anterior STG and the right IFC, as well as in the IPL. Importantly, fMRI RS effects in all of these areas were strongly modulated by the probability of stimulus repetition: auditory fMRI RS was reduced or not present in blocks with low repetition probability. Our results revealed that auditory fMRI RS in higher-level voice-selective cortical regions is modulated by repetition probabilities and thus suggest that in audition, similarly to the visual modality, processing of sensory information is shaped by stimulus expectation processes.