Integration of Text and Graph-based Features for Detecting Mental Health Disorders from Voice
With the availability of voice-enabled devices such as smart phones, mental health disorders could be detected and treated earlier, particularly post-pandemic. The current methods involve extracting features directly from audio signals. In this paper, two methods are used to enrich voice analysis for depression detection: graph transformation of voice signals, and natural language processing of the transcript based on representational learning, fused together to produce final class labels. The results of experiments with the DAIC-WOZ dataset suggest that integration of text-based voice classification and learning from low level and graph-based voice signal features can improve the detection of mental disorders like depression.
Code (1)
Tasks
Depression DetectionSimilar Papers 제목 키워드 기반
Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning
Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are susceptible to sophis…
Intrusion DetectionTime SeriesSINAI at SemEval-2019 Task 3: Using affective features for emotion classification in textual conversations
Detecting emotions in textual conversation is a challenging problem in absence of nonverbal cues typically associated with emotion, like fa- cial expression or voice modulations. How- ever, more and more users are using …
Emotion ClassificationGeneral ClassificationSAC-Net: Spatial Attenuation Context for Salient Object Detection
This paper presents a new deep neural network design for salient object detection by maximizing the integration of local and global image context within, around, and beyond the salient objects. Our key idea is to adaptiv…
Objectobject-detectionObject DetectionRGB Salient Object Detection+1Unveiling Context-Related Anomalies: Knowledge Graph Empowered Decoupling of Scene and Action for Human-Related Video Anomaly Detection
Detecting anomalies in human-related videos is crucial for surveillance applications. Current methods primarily include appearance-based and action-based techniques. Appearance-based methods rely on low-level visual feat…
Anomaly DetectionVideo Anomaly DetectionStructural-Temporal Coupling Anomaly Detection with Dynamic Graph Transformer
Detecting anomalous edges in dynamic graphs is an important task in many applications over evolving triple-based data, such as social networks, transaction management, and epidemiology. A major challenge with this task i…
Anomaly DetectionEpidemiology