Deep learning-based automated speech detection as a marker of social functioning in late-life depression

Abstract

Background Late-life depression (LLD) is associated with poor social functioning. However, previous research uses bias-prone self-report scales to measure social functioning and a more objective measure is lacking. We tested a novel wearable device to measure speech that participants encounter as an indicator of social interaction. Methods Twenty nine participants with LLD and 29 age-matched controls wore a wrist-worn device continuously for seven days, which recorded their acoustic environment. Acoustic data were automatically analysed using deep learning models that had been developed and validated on an independent speech dataset. Total speech activity and the proportion of speech produced by the device wearer were both detected whilst maintaining participants' privacy. Participants underwent a neuropsychological test battery and clinical and self-report scales to measure severity of depression, general and social functioning. Results Compared to controls, participants with LLD showed poorer self-reported social and general functioning. Total speech activity was much lower for participants with LLD than controls, with no overlap between groups. The proportion of speech produced by the participants was smaller for LLD than controls. In LLD, both speech measures correlated with attention and psychomotor speed performance but not with depression severity or self-reported social functioning. Conclusions Using this device, LLD was associated with lower levels of speech than controls and speech activity was related to psychomotor retardation. We have demonstrated that speech activity measured by wearable technology differentiated LLD from controls with high precision and, in this study, provided an objective measure of an aspect of real-world social functioning in LLD.

Natural Language Processing as an Emerging Tool to Detect Late-Life Depression

“…Table 1 summarizes recent literature on the topic and highlights different approaches used to collect and analyze speech data. While it is difficult to draw conclusions based on heterogeneous samples and methods, automated speech analysis is proving to be a promising means to tap into cognitive and depressive symptoms in LLD and can be readily adapted for naturalistic settings ( 55 ). Encouraging findings indicate that vocal measures can predict high and low depression scores in LLD between 86 and 92% of the time ( 54 ).…”

“…Preliminary studies have shown that integration of real-time audiovisual analysis into telemedicine platforms may be a feasible method of detecting an individual's emotional state ( 82 ). Additionally, the use of smartphone and wearables technology to record these features have also demonstrated feasibility and acceptability in initial pilot studies ( 55 , 83 ).…”

Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care

“…Wearable devices are another source of acoustic data that can provide personalized clinical services. 21 Therefore, exploring key AI-based scientific methods which are also technical components would further demonstrate their use in various applications.…”

Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks

“…The first approach consists of the extraction of acoustic features from which it is not possible to reconstruct the raw speech signal. A good example is [ 51 ], where the used audio characteristics are the percentage of speech detected in a given time period, and the percentage of speech uttered by the patient, and therefore, it is not necessary to store the whole raw recordings. When the system uses features that allow the recovery of the original speech signal, a second approach can be applied, consisting of the extraction and encryption of the acoustic characteristics in the local device and their transmission to a secure server where further analysis is done [ 49 ].…”