Authors : Harshith Manoharan; Keerthana R E; N. Selvaganesh; Logeswari P

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/bddwjed6

DOI : https://doi.org/10.38124/ijisrt/25may894

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The growing need for accessible mental health support highlights the importance of innovative digital solutions. Many individuals struggle to manage emotions, leading to heightened stress, anxiety, and a decline in well-being. Traditional methods like journaling or therapy, while beneficial, can often feel time-consuming, intimidating, or inaccessible. Current mental health apps frequently fall short, lacking emotional analysis. There is a rising demand for a non-intrusive, user- friendly solution can monitor emotions and provide meaningful insights outside conventional therapy. Mind Your Mind addresses the gap with a voice-based journaling system powered by emotional analysis. Using advanced speech processing, the platform evaluates tone, pitch, and sentiment to assess emotional states as users speak naturally by employing an AI- driven emotion recognition model, integrating Mel- Frequency Cepstral Coefficients (MFCC), Mel-Spectrograms, and Convolutional Neural Networks (CNNs) for accurate pattern recognition. The model achieves an accuracy of 92.3%, enabling reliable emotional detection. Users interact through an intuitive web interface, recording their thoughts and receiving immediate, actionable mood insights in textual format.

Keywords : Emotion Recognition, Mental Health, Speech Analysis, Voice Journaling.

References :

1. Barhoumi, Chawki, and Yassine BenAyed. "Real-time speech emotion recognition using deep learning and data augmentation." Artificial Intelligence Review 58.2 (2024): 49.
2. Sayis, Batuhan, and Hatice Gunes. "Technology-assisted journal writing for improving student mental wellbeing: Humanoid robot vs. voice assistant." Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction.
3. Chawla, Shreya, and Sneha Saha. "Exploring perceptions of psychology students in Delhi-NCR Region towards using mental health apps to promote resilience: a qualitative study." BMC Public Health 24.1 (2024): 2000.
4. Olawade, David B., et al. "Enhancing mental health with Artificial Intelligence: Current trends and future prospects." Journal of Medicine, Surgery, and Public Health (2024): 100099.
5. Liu, Z. "Online hate speech on Twitter from the perspective of pragmatics." International Journal of Social Sciences and Public Administration 4.1 (2024): 322-326.
6. Simmons, Natalie, Lewis Goodings, and Ian Tucker. "Experiences of using mental health Apps to support psychological health and wellbeing." Journal of Applied Social Science 18.1 (2024): 32–44.
7. Aipenova, Aziza, and Seitmukhanova Almira. "Mental Health in the Digital Age: Balancing Connectivity and Well-Being." Journal of Spirituality in Mental Health. 7 (2024).
8. Hamdoun, Salah, et al. "AI-based and digital mental health apps: Balancing need and risk." IEEE Technology and Society Magazine 42.1 (2023): 25–36.
9. Tucker, Ian, Katherine Easton, and Rebecca Prestwood. "Digital community assets: Investigating the impact of online engagement with arts and peer support groups on mental health during COVID‐19." Sociology of Health & Illness 45.3 (2023): 666–683.
10. Pan, Jiahui, et al. "Multimodal emotion recognition based on facial expressions, speech, and EEG." IEEE Open Journal of Engineering in Medicine and Biology (2023).
11. Chamishka, Sadil, et al. "A voice-based real-time emotion detection technique using recurrent neural network empowered feature modelling." Multimedia Tools and Applications 81.24 (2022): 35173–35194.
12. Ravuri, Vinesh, Ricardo Gutierrez-Osuna, and Theodora Chaspari. "Preserving mental health information in speech anonymization." 2022 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). IEEE, 2022.
13. Andayani, Felicia, et al. "Hybrid LSTM-transformer model for emotion recognition from speech audio files." IEEE Access 10 (2022): 36018–36027.
14. Mertens, Esther CA, et al. "Parallel changes in positive youth development and self-awareness: The role of emotional self-regulation, self-esteem, and self-reflection." Prevention Science 23.4 (2022): 502–512.
15. Kakuba, Samuel, Alwin Poulose, and Dong Seog Han. "Deep learning-based speech emotion recognition using multi-level fusion of concurrent features." IEEE Access 10 (2022): 125538–125551.
16. Sun, Licai, et al. "Multimodal cross-and self-attention network for speech emotion recognition." ICASSP 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021.
17. Muppidi, Aneesh, and Martin Radfar. "Speech emotion recognition using quaternion convolutional neural networks." ICASSP 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021.
18. Sajjad, Muhammad, and Soonil Kwon. "Clustering-based speech emotion recognition by incorporating learned features and deep BiLSTM." IEEE access 8 (2020): 79861-79875.
19. Zhang, Jianhua, et al. "Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review." Information fusion 59 (2020): 103-126.
20. Yao, Zengwei, et al. "Speech emotion recognition using fusion of three multi-task learning-based classifiers: HSF-DNN, MS-CNN and LLD-RNN." Speech Communication 120 (2020): 11-19.
21. Nasri, M. A., et al. "Face emotion recognition from static image based on convolution neural networks." 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). IEEE, 2020.
22. Mellouk, Wafa, and Wahida Handouzi. "Facial emotion recognition using deep learning: review and insights." Procedia Computer Science 175 (2020): 689–694.
23. Twenge, Jean M., et al. "Age, period, and cohort trends in mood disorder indicators and suicide-related outcomes in a nationally representative dataset, 2005–2017." Journal of Abnormal Psychology 128.3 (2019): 185.
24. Mirsamadi, Seyedmahdad, Emad Barsoum, and Cha Zhang. "Automatic speech emotion recognition using recurrent neural networks with local attention." 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017.
25. Huang, Yongrui, et al. "Fusion of facial expressions and EEG for multimodal emotion recognition." Computational Intelligence and Neuroscience 2017.1 (2017): 2107451.