Research Area: Machine Learning
Depression is a leading cause of disease burden worldwide, however there is an unmet need for screening and diagnostic measures that can be widely deployed in real-world environments. Voice-based diagnostic methods are convenient, non-invasive to elicit, and can be collected and processed in near real-time using modern smartphones, smart speakers, and other devices. Studies in voice-based depression detection to date have primarily focused on laboratory-collected voice samples, which are not representative of typical user environments or devices. This paper conducts the first investigation of voice-based depression assessment techniques on real-world data from 887 speakers, recorded using a variety of different smartphones. Evaluations on 16 hours of speech show that conservative segment selection strategies using highly thresholded voice activity detection, coupled with tailored normalization approaches are effective for mitigating smartphone channel variability and background environmental noise. Together, these strategies can achieve F1 scores comparable with or better than those from a combination of clean recordings, a single recording environment and long utterances. The scalability of speech elicitation via smartphone allows detailed models dependent on gender, smartphone manufacturer and/or elicitation task. Interestingly, results herein suggest that normalization based on these criteria may be more effective than tailored models for detecting depressed speech.
Keywords:
Depression Detection
Diverse Smartphones
Natural Environmental Conditions
Machine Learning
Deep Learning
Author(s) Name: Zhaocheng Huang, J. Epps, Dale Joachim, M. Chen
Journal name: Computer Science
Conferrence name:
Publisher name: Semantic Scholar
DOI: 10.21437/Interspeech.2018-1743
Volume Information:
