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@article{201065,
        author = {Mrs. Veerendeswari J and Sangeetha P and Snega R and Ilakiya M and Deepika S},
        title = {Voice-Based Depression & Anxiety Detection using Quantum NLP},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {299-308},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201065},
        abstract = {Mental health disorders such as depression and anxiety have become increasingly prevalent in modern society, affecting individuals across different age groups and backgrounds. These conditions have a significant impact on emotional wellbeing, productivity, and overall quality of life. Despite their seriousness, early detection remains a major challenge due to factors such as social stigma, lack of awareness, and limited access to professional healthcare services. As a result, many individuals remain undiagnosed until the condition becomes severe. Traditional diagnostic approaches primarily rely on self-reporting, psychological assessments, and clinical interviews. While these methods are effective, they are often subjective and depend heavily on the individual’s willingness to share personal experiences. In many cases, symptoms may be overlooked or misinterpreted, leading to delayed diagnosis and treatment. This highlights the need for automated, objective, and non-invasive methods for early detection of mental health conditions. In this project, a voice-based depression and anxiety detection system is proposed, which utilizes speech signals as the primary source of input. Human voice carries rich emotional and psychological information, including variations in tone, pitch, speech rate, and energy. By analyzing these acoustic features, it is possible to identify patterns associated with emotional states. Additionally, speech-to-text conversion enables the extraction of linguistic features, providing deeper insight into the semantic content of speech. The proposed system integrates Quantum Natural Language Processing (QNLP) to enhance the analysis of textual data. QNLP leverages principles of quantum mechanics, such as superposition and entanglement, to represent and process language in high dimensional spaces. This approach allows for better modeling of contextual relationships, ambiguity, and complex semantic structures compared to traditional Natural Language Processing techniques. Furthermore, the system employs a hybrid approach that combines classical machine learning models with quantum inspired representations. This integration improves the system’s ability to capture both acoustic and linguistic patterns effectively. As a result, the model can achieve higher accuracy and better contextual understanding when identifying signs of depression and anxiety from speech data. Experimental results demonstrate that the proposed system performs effectively in detecting emotional states, showing improved accuracy and reliability compared to conventional methods. The findings suggest that voice-based analysis, combined with Quantum NLP, has strong potential for real-world applications in mental health monitoring. This approach can contribute to the development of scalable, accessible, and efficient tools for early diagnosis and intervention.},
        keywords = {Depression Detection, Anxiety Detection, Voice Analysis, Quantum NLP, Machine Learning},
        month = {May},
        }