RESUMO
BACKGROUND: Providing Psychotherapy, particularly for youth, is a pressing challenge in the health care 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, presents an emerging approach to address these challenges. Speech can carry vital information about emotional states, which can be used to improve mental health care 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 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 5 or higher, at the highest risk for chronic disease and social or 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. In total, 4 highly trained therapists classified audio segments based on a scoring process of 4 emotions and their intensity levels for each of the 4 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 4 different emotions, achieved test-set precision and recall of 83% for each. CONCLUSIONS: Automated emotion detection from patients' speech using artificial intelligence 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 health care services. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/51912.
RESUMO
OBJECTIVE: Research suggests that antiimmigrant policies enacted in the United States, magnified during the 2016-2020 period, propagate widespread trauma across communities of immigrants (von Werthern et al., 2018). While these policies harm all groups of immigrants, structural conditions (e.g., lack of documentation status, race, ethnicity, country of origin, and other social and legal determinants) shape how they are experienced. To address the widespread traumatic harm inflicted by racist and xenophobic policies, a group of leaders from eight Divisions of the American Psychological Association (APA) and the National Latinx Psychological Association (NLPA) launched an Interdivisional Immigration Project (IIP). METHOD: The IIP served to develop a model for collaborative advocacy, bringing together mental health providers (i.e., psychologists, social workers), allied professionals, and immigration activists from community organizations across the country. This model was developed over the course of 1 year, coinciding with the global coronavirus disease 2019 (COVID-19) pandemic and the amplified movement for racial justice. RESULTS: This article describes the key components of the IIP collaborative advocacy model: (a) structuring leadership in a democratic and egalitarian manner, (b) centering and uplifting immigrant voices, (c) forming teams across five U.S. regions, (d) facilitating critical dialogues grounded in liberatory practices, (e) centering trauma and empowerment, and (f) developing advocacy strategies. The IIP collaborative advocacy model is informing advocacy to protect immigrants from harm. DISCUSSION: This model may be used as the basis for ongoing humane immigration policy activism that centers the voices of community activists, and that pushes psychologists and allied professionals to use their positionality to support community-based efforts. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
