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@article{174942,
        author = {Ashkaan Khan and Harsh Sharma and Kanika Gautam and Mohit Sahani and Dr. Sudhir Dawra and Mohit Singh Yadav},
        title = {Speech Emotion Recognition System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1355-1361},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174942},
        abstract = {Understanding human emotions from speech is a fundamental challenge in human-computer interaction. The primary problem in Speech Emotion Recognition (SER) lies in accurately identifying emotions despite variations in speakers, accents, background noise, and recording conditions. Traditional emotion recognition methods rely on facial expressions and physiological signals, but speech-based recognition offers a non-intrusive and effective alternative.
This research explores various feature extraction techniques such as Mel-Frequency Cepstral Coefficients (MFCCs), Chroma Features, and Spectrograms to capture emotional cues from speech. Additionally, machine learning classifiers like Support Vector Machines (SVM) and deep learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are analyzed to improve classification accuracy.},
        keywords = {Speech Emotion Recognition, Machine Learning, Deep Learning, Feature Extraction, Human-Computer Intelligence.},
        month = {April},
        }