Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Joy, Salna; Krishna, B.V. Santhosh; Rajesh, Siddharth; Neethu, P.S. Title A name given to the resource Deep Learning for Mental Health: Attention-Driven Multilayer CNN for Audio Depression Detection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Conference on Intelligent Communication Networks and Computational Techniques, ICICNCT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICICNCT66124.2025.11233143" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICICNCT66124.2025.11233143</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030492475?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030492475?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Joy S., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, India; Krishna B.V.S., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, India; Rajesh S., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, India; Neethu P.S., School of Engineering and Technology, Christ University, Department of Aiml and Data Science, Bengaluru, India Description An account of the resource Depressive Disorder is a common mental health problem that affects millions of people around the world. This study proposes a Self-attention based Multi-layer Convolutional Neural Network (CNN) model to perform enhanced depression detection from audio modality. The model employs a diverse array of filters, kernel sizes, and pooling strategies across multiple CNN layers to capture local features, while the attention mechanism prioritizes emotionally salient parts of the speech signal, such as regions of low energy and lengthened pauses by assigning higher weights. Measured against the RAVDESS and TESS emotional speech datasets, the method attains an F1 score of 0.81, an accuracy of 83% and ROC-AUC of 0.96 when using attention, beating the baseline CNN model, F1 score of 0.77 and 83% accuracy without attention. The results demonstrate the effectiveness of attention-enhanced architectures in detecting depressive cues from speech and support the feasibility of developing real-world, speech-based mental health screening tools. 2025 IEEE. Subject The topic of the resource attention mechanism; audio depression detection; deep learning; mental health screening; multi-layer convolutional neural networks (CNN) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158623-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online