Objectively Quantifying Pediatric Psychiatric Severity Using Artificial Intelligence, Voice Recognition Technology, and Universal Emotions: Pilot Study for Artificial Intelligence-Enabled Innovation to Address Youth Mental Health Crisis (Preprint)

Abstract

BACKGROUND Providing Psychotherapy, particularly for youth, is a pressing challenge in the healthcare system. Traditional methods are resource-intensive, and there is a need for objective benchmarks to guide therapeutic interventions. Automated emotion detection from speech, using artificial intelligence (AI), presents an emerging approach to address these challenges. Speech can carry vital information about emotional states, which can be utilized to improve mental healthcare services, especially when the person is suffering. OBJECTIVE This study aims to develop and evaluate automated methods for detecting the intensity of emotions (anger, fear, sadness, and happiness) in audio recordings of patients’ speech. We also demonstrate the viability of deploying the models. Our model was validated in a previous publication by Alemu et al. (2023) with limited voice samples. This follow-up study used significantly more voice samples to validate the previous model. METHODS We used audio recordings of patients, specifically children with high Adverse Childhood Experience ACE scores; the average ACE score was five or higher, at the highest risk for chronic disease and social/emotional problems; only 1 in 6 have a score of 4 or above. The patients' structured voice sample was collected by reading a fixed script. Four highly trained therapists classified audio segments based on a scoring process of four emotions and their intensity levels for each of the four different emotions. We experimented with various preprocessing methods, including denoising, voice-activity detection, and diarization. Additionally, we explored various model architectures, including convolutional neural networks (CNNs) and transformers. We trained emotion-specific transformer-based models and a generalized CNN-based model to predict emotion intensities. RESULTS The emotion-specific transformer-based model achieved a test-set precision and recall of 86% and 79%, respectively, for binary emotional intensity classification (high or low). In contrast, the CNN-based model, generalized to predict the intensity of four different emotions, achieved test-set precision and recall of 83% for each. CONCLUSIONS Automated emotion detection from patients' speech using AI models is found to be feasible, leading to a high level of accuracy. The transformer-based model exhibited better performance in emotion-specific detection, while the CNN-based model showed promise in generalized emotion detection. These models can serve as valuable decision-support tools for pediatricians and mental health providers to triage youth to appropriate levels of mental healthcare services.