Bipolar Disorder Identification using Speech Emotion Recognition and Sequential Language Pattern Analysis

Bipolar disorder is a complex mental health condition characterized by extreme mood variations, including manic and
depressive episodes. Early identification of such mood fluctuations remains a significant challenge in clinical practice due to the
subjective nature of diagnosis and dependence on patient self-reporting.
This project proposes an intelligent system that combines Speech Emotion Recognition (SER) and Sequential Language Pattern
Analysis to identify bipolar disorder symptoms in a more objective and data-driven manner. The system analyzes vocal features
such as tone, pitch, and intensity, along with linguistic patterns like sentence structure, word usage, and coherence across time.
By integrating audio signal processing with Natural Language Processing (NLP) techniques, the model aims to detect emotional
and behavioral changes that correspond to different bipolar states. The proposed system has the potential to support early diagnosis,
continuous monitoring, and improved mental healthcare outcomes.