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Speech Recognition-Based Prediction for Mental Health and Depression: A Review

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Proceedings of Congress on Control, Robotics, and Mechatronics (CRM 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 364))

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Abstract

A person with a mental disorder exhibits a significant disturbance in his or her behavior. Generally, mental disorders are associated with distress or impairment of normal functioning. Lack of adequate resources and facilities, as well as a lack of awareness of the symptoms of mental illness, prevent people from getting the help they need. The ability to assess depression through speech is a critical factor in improving the diagnosis and treatment of depression. The spoken language is said to provide access to the mind, and a wide range of speech capture and processing technologies can be used to analyze mental health. Speech processing is about recognizing spoken words. The automatic recognition and extraction of information from speech enables the determination of some physiological characteristics that make a speaker unique to identify their mental health status. In this paper, we describe how mental health-related problems can be predicted by speech processing. This paper identifies the gaps in the literature review that lead to the proposed methodology.

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References

  1. Liu, S., Vahedian, F., Hachen, D., Lizardo, O., Poellabauer, C., Striegel, A., Milenković, T.: Heterogeneous network approach to predict individuals’ mental health. ACM Trans. Knowl. Discov. Data 15(2), Article 25

    Google Scholar 

  2. Stasak, B., Huang, Z., Joachim, D., Epps, J.: Automatic elicitation compliance for short-duration speech based depression detection. In: ICASSP 2021—2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 978-1-7281-7605-5/20/$31.00 ©2021 IEEE. https://doi.org/10.1109/ICASSP39728.2021.9414366

  3. https://www.who.int/news-room/factsheets/detail/mental-disorders

  4. https://www.ideasforindia.in/topics/human-development/understanding-india-s-mental-healthcrisis.html#:~:text=In%202017%2C%20the%20President%20of,49%20million%20from%20anxiety%20disorders

  5. Priya, A., Garga, S., Tigga, N.P.: Predicting anxiety, depression and stress in modern life using machine learning algorithm. In: International Conference on Computational Intelligence and Data Science (ICCIDS 2019). Procedia Comput. Sci. 167, 1258–1267 (2020)

    Google Scholar 

  6. Nanath, K., Balasubramanian, S., Shukla, V., Islam, N., Kaitheri, S.: Developing a mental health index using a machine learning approach: assessing the impact of mobility and lockdown during the COVID-19 pandemic. Technol. Forecast. Soc. Change 178, 121560 (2022)

    Google Scholar 

  7. Alghowinem, S., Goecke, R., Wagner, M., Epps, J., Hyett, M., Parker, G., Breakspear, M.: Multimodal depression detection: fusion analysis of paralinguistic, head pose and eye gaze behaviors. IEEE Trans. Affect. Comput. 9(4) (2018)

    Google Scholar 

  8. Ríssola, E.A. Aliannejadi, M., Crestani, F.: Mental disorders on online social media through the lens of language and behaviour: analysis and visualization. Inf. Process. Manag. 59, 102890 (2022)

    Google Scholar 

  9. Sarkara, A., Singh, A., Chakraborty, R.: A deep learning-based comparative study to track mental depression from EEG data. Neurosci. Inform. 2, 772–5286 100039 (2022)

    Google Scholar 

  10. Liu, S., Vahedian, F., Hachen, D., Lizardo, O., Poellabauer, C., Striegel, A., Milenković, T.: Heterogeneous network approach to predict individuals’ mental health. ACM Trans. Knowl. Discov. Data 15(2), Article 25. Publication date: April 2021

    Google Scholar 

  11. Dong, Y., Yang, X.: A hierarchical depression detection model based on vocal and emotional cues. Neurocomputing 441, 279–290 (2021)

    Article  Google Scholar 

  12. Ye, J., Yu, Y., Wang, Q., Li, W., Liang, H., Zheng, Y., Fu, G.: Multi-modal depression detection based on emotional audio and evaluation text. J. Affect. Disord. 295, 904–913 (2021)

    Google Scholar 

  13. Di Matteo, D., Fotinos, K., Lokuge, S., Mason, G., Sternat, T., Katzman, M.A., Rose, J.: Automated screening for social anxiety, generalized anxiety, and depression from objective smartphone-collected data: cross-sectional study. J. Med. Internet Res. 23(8), e28918 (2021)

    Google Scholar 

  14. Amanat, A., Rizwan, M., Javed, A.R., Abdelhaq, M., Alsaqour, R., Pandya, S., Uddin, M.: Deep learning for depression detection from textual data. Electronics 11, 676 (2022). https://doi.org/10.3390/electronics11050676

  15. Gupta, M., Vaikole, S.: Audio signal based stress recognition system using AI and machine learning. J Algebraic Stat. 13(2), 1731–1740 (2022)

    Google Scholar 

  16. Rejaibi, E., Komaty, A., Meriaudeau, F., Agrebi, S., Othmani, A.: MFCC-based recurrent neural network for automatic clinical depression recognition and assessment from speech. Biomed. Signal Process. Control 71, 103107 (2022)

    Article  Google Scholar 

  17. Rutowski, T., Shriberg, E., Harati, A., Lu, Y., Oliveira, R., Chlebek, P.: Cross-demographic portability of deep NLP-based. depression models. In: 2021 IEEE Spoken Language Technology Workshop (SLT), 978-1-7281-7066-4/20/$31.00 ©2021 IEEE. https://doi.org/10.1109/SLT48900.2021.9383609

  18. Schultebraucks, K., Yadav, V., Shalev, A.Y. Bonanno, G.A., Galatzer-Levy, I.R.: Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood. PsychologicalMedicine 1–11. https://doi.org/10.1017/S0033291720002718

  19. Aloshban, N., Esposito, A., Vinciarelli, A., What you say or how you say it? Depression detection through joint modeling of linguistic and acoustic aspects of speech. Cognitive Comput. https://doi.org/10.1007/s12559-020-09808-3

  20. El Shazly, R.: Effects of artificial intelligence on English speaking anxiety and speaking performance: a case study. Expert Syst. 38, e12667 (2021)

    Google Scholar 

  21. Garoufis, C., Zlatintsi, A., Filntisis, P.P., Efthymiou, N., Kalisperakis, E., Garyfalli. V., Karantinos, T., Mantonakis, L., Smyrnis N., Maragos, P.: An unsupervised learning approach for detecting relapses from spontaneous speech in patients with psychosis. In: Proceedings 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Athens, Greece, July 2021

    Google Scholar 

  22. Daus, H., Backenstrass, M. Feasibility and acceptability of a mobile-based emotion recognition approach for bipolar disorder. Int. J. Interact. Multim. Artif. Intell. 7(2)

    Google Scholar 

  23. Sharma, A., Verbeke, W.J.M.I.: Improving diagnosis of depression with XGBOOST machine learning model and a large biomarkers Dutch Dataset (n = 11,081). Front. Big Data. 3, Article 15 (2020). www.frontiersin.org

  24. Wang, H., Liu, Y., Zhen, X., Tu. X.: Depression speech recognition with a three-dimensional convolutional network. Front. Hum. Neurosci. 15, Article 713823 (2021). www.frontiersin.org

  25. Villatoro-Tello, E., Pavankumar Dubagunta, S., Fritsch, J., Ramírez-de-la-Rosa, G., Motlicek, P., Magimai-Doss, M.: Late Fusion of the available lexicon and raw waveform-based acoustic modeling for depression and dementia recognition

    Google Scholar 

  26. Araño, K.A., Gloor,· P., Orsenigo, C., Vercellis, C.: When old meets new: emotion recognition from speech signals. Cognitive Comput. 13, 771–783 (2021). https://doi.org/10.1007/s12559-021-09865-2

  27. Mou, L., Zhou, C., Zhao, P., Nakisa, B., Rastgoo, M.N., Jain, R., Gao, W.: Driver stress detection via multimodal fusion using attention-based CNN-LSTM. Expert Syst. Appl. 173, 114693 (2021)

    Google Scholar 

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Authors and Affiliations

  1. Dr. D. Y. Patil Institute of Technology, Pimpri Pune, Maharashtra, India

    Priti Gaikwad & Mithra Venkatesan

Authors
  1. Priti Gaikwad
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  2. Mithra Venkatesan
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Correspondence to Priti Gaikwad .

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Editors and Affiliations

  1. Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Pradeep Kumar Jha

  2. Department of Mechanical Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Brijesh Tripathi

  3. Department of Mechanical and Mechatronic Engineering, UCSI University, Cheras, Kuala Lumpur, Malaysia

    Elango Natarajan

  4. Department of Computer Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

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Gaikwad, P., Venkatesan, M. (2024). Speech Recognition-Based Prediction for Mental Health and Depression: A Review. In: Jha, P.K., Tripathi, B., Natarajan, E., Sharma, H. (eds) Proceedings of Congress on Control, Robotics, and Mechatronics. CRM 2023. Smart Innovation, Systems and Technologies, vol 364. Springer, Singapore. https://doi.org/10.1007/978-981-99-5180-2_2

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  • DOI: https://doi.org/10.1007/978-981-99-5180-2_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5520-6

  • Online ISBN: 978-981-99-5180-2

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