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@article{176211,
        author = {Bussa Sai Lahari and Vemula Ashwitha and Bhukya Geethika and Cheviti Uday Kumar and Dr. G . Aparna},
        title = {EMOTIONS RECOGNITION BY SPEECH AND FACIAL EXPRESSIONS ANALYSIS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5973-5981},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176211},
        abstract = {Understanding human emotions is crucial in various real-world applications, such as mental health monitoring, human-computer interaction, and customer service automation. This project addresses the challenge of automatic emotion recognition by leveraging deep learning techniques on two key modalities: facial expressions and speech signals. Traditional methods often struggle with the variability of human emotions across different individuals and environments. To overcome this, we employ Convolutional Neural Networks (CNNs) for facial image and speech audio classification, allowing the system to learn discriminative features from both visual and auditory data.
The facial CNN is trained on a dataset of facial images to identify emotions based on spatial features such as expressions, eye movements, and mouth shapes. Meanwhile, the speech CNN processes audio spectrograms to capture variations in tone, pitch, and rhythm that correlate with different emotional states. This approach enables a more reliable emotion recognition system by ensuring that emotions can still be detected even when one modality is unavailable or ambiguous. By addressing the growing demand for intelligent emotion-aware systems, this project contributes to fields such as healthcare (for stress and depression detection), customer service (for sentiment analysis in call centers), and smart assistants (for improving user experience through emotion-adaptive responses). The results lay the foundation for developing a more comprehensive multimodal emotion recognition system, bridging the gap between artificial intelligence and human-like emotional understanding.},
        keywords = {Emotion Recognition, Deep Learning, Convolutional Neural Networks (CNNs), Facial Expression Analysis, Speech Signal Processing, Multimodal Emotion Detection, Audio Spectrogram Classification, Mental Health Monitoring, Sentiment Analysis},
        month = {April},
        }