Mental health services are no longer taboo, at least by non-BIPOC and LBGT communities. Recent data suggest “In 2019, 19.2% of adults had received any mental health treatment in the past 12 months, including 15.8% who had taken prescription medication for their mental health and 9.5% who received counseling or therapy from a mental health professional.” (Terlizzi EP, Zablotsky B. Mental health treatment among adults: the United States, 2019). There is a tendency towards decentralization and virtual care as part of the pandemic-related changes in the model of care. For example, Talkspace Reports Third Quarter 2021 Financial Results indicate this trend to virtual care: Talkspace has generated $110 million in delivering approximately 600,000 sessions in the past year. While these trends point to improved access to care, they do not address the long-standing issues of quality and outcomes.
The next phase of the evolution of the mental health delivery system of care is leveraging innovative technology to address quality and disparities in treatment outcomes. To this end, the use of Voice in developing biomarkers of mental health disorders is one area of progress. The emergence of technological advancements, such as IoT, AI, deep learning, unobstructed computing, improved human-computer interfaces, and cutting-edge networking technologies are anticipated to enhance the performance of emotion detection and recognition systems. IoT and AI’s combined features improved the emotion detection and recognition technology with enhancements in collected data uploaded on the cloud and analyzed across the globe. Previously, the data could not be accessed online, was portable, and had a high cost.
Real-time emotion detection, emotion-sensing, and analytics technology transform the healthcare industry. The healthcare industry has the vision to create more innovative diagnostic tools with patients’ emotions. Emotion detection and recognition can be used to detect several emotions exhibited by patients during their stay in the facility and analyze the data to determine how the patients are feeling. The analysis results will help identify where the patients need more attention if they are in pain or stressed.
The link between trauma/stress and voice types is related to the automatic nervous system changes, including the disruption of speech-based characteristics. This is partly due to muscle tensions. Further evidence exists for this relation; for example, a correlation has been found between neural activity in the gamma-aminobutyric acid (GABA) neurotransmitter, susceptibility to depression, and changes in muscle tonality. These changes related to muscle tonality produce distinct utterances indicating the presence of negative mood/depressive symptoms.