International Journal For Multidisciplinary Research

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AI-Powered Mental Health Assessment Using Speech and Text Analysis

Author(s) Ms. Sakshi Pramod Patil, Ms. Samruddhi Sanjay Faratkhane
Country India
Abstract More than 280 million people worldwide suffer from
mental health conditions like anxiety and depression. Despite
increased awareness, early diagnosis remains challenging due
to reliance on conventional methods like clinical interviews
and self-reported questionnaires, which are often subjective and
inaccessible. This study proposes a scalable, objective, and non
invasive AI-powered mental health assessment framework that
utilizes Natural Language Processing (NLP) and speech signal
analysis.
The system combines transformer-based models like BERT
for understanding text and LSTM-based models for analyzing
speech patterns including pitch, jitter, shimmer, and MFCCs.
Using datasets such as Reddit, therapy transcripts, and DAIC
WOZ, the system is trained to detect early signs of depression
and anxiety. A multimodal fusion layer further integrates both
modalities to enhance predictive accuracy.
The final product is a real-time, mobile-compatible application
that accepts voice or text inputs for screening and provides
feedback, risk assessment, and recommendations. Ethical consid
erations including data privacy, explainability, and algorithmic
fairness are addressed throughout. The proposed system is aimed
at reducing the diagnostic gap, supporting early intervention, and
making mental health tools more accessible.
Keywords Artificial Intelligence, Mental Health, Depression Detection, Natural Language Processing, Speech Analysis, BERT, LSTM, Multimodal Fusion
Field Computer Applications
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-04-30
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.41988
Short DOI https://doi.org/g9g739
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