Weizhe Xu

Mobile phone sensing is increasingly being used in clinical research studies to assess a variety of mental health conditions (e.g., depression, psychosis). However, in-the-wild speech analysis -- beyond conversation detecting -- is a missing component of these mobile sensing platforms and studies. We augment an existing mobile sensing platform with a daily voice diary to assess and predict the severity of auditory verbal hallucinations (i.e., hearing sounds or voices in the absence of any speaker), a condition that affects people with and without psychiatric or neurological diagnoses. We collect 4809 audio diaries from N=384 subjects over a one-month-long study period. We investigate the performance of various deep-learning architectures using different combinations of sensor behavioral streams (e.g., voice, sleep, mobility, phone usage, etc.) and show the discriminative power of solely using audio recordings of speech as well as automatically generated transcripts of the recordings; specifically, our deep learning model achieves a weighted f-1 score of 0.78 solely from daily voice diaries. Our results surprisingly indicate that a simple periodic voice diary combined with deep learning is sufficient enough of a signal to assess complex psychiatric symptoms (e.g., auditory verbal hallucinations) collected from people in the wild as they go about their daily lives.