Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring

• URL: http://arxiv.org/abs/2208.12812v1
• Date: Fri, 26 Aug 2022 01:14:31 GMT
• Title: Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring
• Authors: Nelly Elsayed, Zag ElSayed, Navid Asadizanjani, Murat Ozer, Ahmed Abdelgawad, Magdy Bayoumi
• Abstract summary: This paper proposes a novel deep learning model based on the gated recurrent neural network and convolution neural network to understand human emotion from speech to improve their IVA services and monitor their mental health.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding human behavior and monitoring mental health are essential to maintaining the community and society's safety. As there has been an increase in mental health problems during the COVID-19 pandemic due to uncontrolled mental health, early detection of mental issues is crucial. Nowadays, the usage of Intelligent Virtual Personal Assistants (IVA) has increased worldwide. Individuals use their voices to control these devices to fulfill requests and acquire different services. This paper proposes a novel deep learning model based on the gated recurrent neural network and convolution neural network to understand human emotion from speech to improve their IVA services and monitor their mental health.

Related papers

• MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders [59.515827458631975]
  Mental health disorders are one of the most serious diseases in the world. Privacy concerns limit the accessibility of personalized treatment data. MentalArena is a self-play framework to train language models.
  arXiv  Detail & Related papers  (2024-10-09T13:06:40Z)
• Risks from Language Models for Automated Mental Healthcare: Ethics and Structure for Implementation [0.0]
  This paper proposes a structured framework that delineates levels of autonomy, outlines ethical requirements, and defines beneficial default behaviors for AI agents. We also evaluate 14 state-of-the-art language models (ten off-the-shelf, four fine-tuned) using 16 mental health-related questionnaires.
  arXiv  Detail & Related papers  (2024-04-02T15:05:06Z)
• PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents [68.50571379012621]
  Psychological measurement is essential for mental health, self-understanding, and personal development. PsychoGAT (Psychological Game AgenTs) achieves statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity.
  arXiv  Detail & Related papers  (2024-02-19T18:00:30Z)
• Challenges of Large Language Models for Mental Health Counseling [4.604003661048267]
  The global mental health crisis is looming with a rapid increase in mental disorders, limited resources, and the social stigma of seeking treatment. The application of large language models (LLMs) in the mental health domain raises concerns regarding the accuracy, effectiveness, and reliability of the information provided. This paper investigates the major challenges associated with the development of LLMs for psychological counseling, including model hallucination, interpretability, bias, privacy, and clinical effectiveness.
  arXiv  Detail & Related papers  (2023-11-23T08:56:41Z)
• LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts [4.6071451559137175]
  Low self-esteem and interpersonal needs have a major impact on depression and suicide attempts. Individuals seek social connectedness on social media to boost and alleviate their loneliness. We introduce a psychology-grounded and expertly annotated dataset, LoST: Low Self esTeem, to study and detect low self-esteem on Reddit.
  arXiv  Detail & Related papers  (2023-06-08T23:52:35Z)
• Memory-Augmented Theory of Mind Network [59.9781556714202]
  Social reasoning requires the capacity of theory of mind (ToM) to contextualise and attribute mental states to others. Recent machine learning approaches to ToM have demonstrated that we can train the observer to read the past and present behaviours of other agents. We tackle the challenges by equipping the observer with novel neural memory mechanisms to encode, and hierarchical attention to selectively retrieve information about others. This results in ToMMY, a theory of mind model that learns to reason while making little assumptions about the underlying mental processes.
  arXiv  Detail & Related papers  (2023-01-17T14:48:58Z)
• Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs [77.88043871260466]
  We show that one of today's largest language models lacks this kind of social intelligence out-of-the box. We conclude that person-centric NLP approaches might be more effective towards neural Theory of Mind.
  arXiv  Detail & Related papers  (2022-10-24T14:58:58Z)
• Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning [55.653944436488786]
  According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD. This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
  arXiv  Detail & Related papers  (2022-07-03T11:33:52Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• Social Behavior and Mental Health: A Snapshot Survey under COVID-19 Pandemic [6.5721468981020665]
  COVID-19 pandemic has changed the way we live, study, socialize and recreate. There are growing researches that leverage online social media analysis to detect and assess user's mental status.
  arXiv  Detail & Related papers  (2021-05-17T21:08:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.