  In recent years, data-driven models have enabled significant advances in
medicine. Simultaneously, voice has shown potential for analysis in precision
medicine as a biomarker for screening illnesses. There has been a growing trend
to pursue voice data to understand neuropsychiatric diseases. In this paper, we
present DigiVoice, a comprehensive feature extraction and analysis pipeline for
voice data. DigiVoice supports raw .WAV files and text transcriptions in order
to analyze the entire content of voice. DigiVoice supports feature extraction
including acoustic, natural language, linguistic complexity, and semantic
coherence features. DigiVoice also supports machine learning capabilities
including data visualization, feature selection, feature transformation, and
modeling. To our knowledge, DigiVoice provides the most comprehensive voice
feature set for data analysis to date. With DigiVoice, we plan to accelerate
research to correlate voice biomarkers with illness to enable data-driven
treatment. We have worked closely with our industry partner, NeuroLex
Laboratories, to make voice computing open source and accessible. DigiVoice
enables researchers to leverage our technology across the domains of voice
computing and precision medicine without domain-specific expertise. Our work
allows any researchers to use voice as a biomarker in their past, current, or
future studies.
