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@article{163640,
        author = {Vinal waghela and Dr. Miral patel and Prof. Rahul Patel},
        title = {A Comparative Analysis of Machine Learning and Deep Learning Techniques for Speech Emotion Recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {1928-1937},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163640},
        abstract = {This review paper explores Speech Emotion Recognition (SER) techniques, comparing traditional Machine Learning (ML) and Deep Learning (DL) approaches. We analyze the strengths and weaknesses of each approach, particularly regarding feature engineering and model complexity. The paper discusses how ML methods rely on handcrafted features like Mel-Frequency Cepstral Coefficients (MFCCs) for emotion classification using algorithms like Support Vector Machines (SVMs) or k-Nearest Neighbors (kNN). Conversely, Deep Learning techniques, particularly Long Short-Term Memory (LSTM) networks, have emerged as powerful tools for SER due to their ability to automatically learn features directly from raw audio data. We examine the trade-offs between interpretability of ML models and the data-driven feature learning capabilities of Deep Learning. Additionally, the paper explores challenges faced by both approaches, including data availability and domain adaptation. Finally, we discuss the potential applications of SER technology across various domains and highlight promising future directions in this evolving field.},
        keywords = {Long Short-Term Memory (LSTM), Mel-frequency cepstral coefficients (MFCCs), Toronto Emotional Speech Synthesis (TESS) dataset},
        month = {},
        }