AI-Based Acoustic Intelligence System for Mental Well-Being

Abstract

The increasing prevalence of mental health challenges and the need for timely intervention have motivated the development of intelligent, emotion-aware support systems. This paper presents an AI-based Acoustic Intelligence System for Mental Well-Being that integrates Speech Emotion Recognition (SER), Natural Language Processing (NLP), and conversational AI to detect and respond to user emotions in real time. The proposed system processes speech input through pre-processing and feature extraction techniques such as Mel-Frequency Cepstral Coefficients (MFCC), pitch, and energy, followed by classification using machine learning and deep learning models. A hybrid approach combining acoustic features and textual sentiment analysis enhances emotion classification accuracy and robustness. The system is implemented using Python, Google Colab, and Streamlit, providing an interactive user interface with modules for memory analysis, entity detection, and contextual conversation management. Experimental results demonstrate reliable performance in recognizing emotions such as happy, sad, angry, and neutral across both speech and text inputs, even under moderate noise conditions. Additionally, the integration of a memory-based context retrieval mechanism enables personalized and context-aware responses. The proposed system highlights the effectiveness of combining SER and NLP for real-time emotional assistance and intelligent mental health monitoring. Rapid identifier: ML, Google Colab, Streamlit

Introduction

The text discusses the growing importance of mental health support systems in response to increasing stress, anxiety, and depression caused by modern lifestyle pressures. Many individuals face barriers to traditional mental healthcare, such as cost, stigma, and limited access to professionals, highlighting the need for intelligent technological solutions.

To address this, the work proposes an AI-driven Acoustic Intelligence System for Mental Well-Being that uses Artificial Intelligence, Natural Language Processing (NLP), and Speech Emotion Recognition (SER) to detect a user’s emotional state from speech. The system captures audio input, preprocesses it by removing noise and normalizing the signal, and then extracts key acoustic features such as MFCC, pitch, tone, and energy.

Machine learning and deep learning models like SVM, Random Forest, CNN, and RNN are used to classify emotions such as stress, happiness, sadness, and anxiety. NLP is also applied to understand textual input and generate supportive, empathetic responses, enabling conversational interaction. The system is trained on labeled speech datasets and is designed to improve emotion detection accuracy over time.

The proposed system aims to provide real-time emotional analysis and supportive feedback, combining speech processing and conversational AI to assist users in managing their mental well-being. It can detect emotional patterns, respond with appropriate suggestions or encouragement, and support mental health monitoring.

The results section indicates that the system effectively identifies emotions from both speech and text and provides meaningful responses. Interfaces such as memory analysis and entity detection help visualize user interactions and improve system interpretability. Overall, the integration of SER, NLP, and AI enables a responsive and empathetic mental health support system.

Conclusion

This work presented an AI-based Acoustic Intelligence System for Mental Well-Being that integrates speech recognition, emotion detection, and NLP-driven conversational intelligence into a unified framework. The proposed methodology effectively combines acoustic feature analysis and textual sentiment understanding to improve the accuracy and stability of emotion classification. Experimental results confirm that the system can reliably recognize multiple emotional states from both speech and text inputs, with improved performance achieved through the hybrid SER–NLP approach. The implementation of a memory module using vector embeddings enables contextual awareness, allowing the system to generate more personalized and meaningful responses during user interactions. Furthermore, the system demonstrated stable real-time speech-to-text conversion and consistent performance under moderate background noise conditions. Overall, the results validate that the proposed system is capable of providing intelligent emotional assistance and monitoring user well-being through adaptive and empathetic responses. Future work can focus on improving model generalization across diverse languages and accents, incorporating multimodal inputs such as facial expressions, and enhancing real-time deployment for scalable mental health support applications.

References

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Copyright

Copyright © 2026 Vulugundam Anitha, A. Shiva Tejaswini, B. Vaijayanthi, Bethi Akhlila, Talveda. Sneha Reddy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.