Face and Speech Emotion Recognition

Volume: 10 | Issue: 02 | Year 2024 | Subscription
International Journal of Image Processing and Pattern Recognition
Received Date: 06/28/2024
Acceptance Date: 09/27/2024
Published On: 2024-10-08
First Page: 35
Last Page: 43

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By: Akshay Shetye, Shruti Chavan, Akshay Parab, Kaushik Patil, and Sumitra Kulkarni

Abstract

The study “faces and speech recognition” underscores the critical role of speech emotion recognition (SER) and its diverse applications across various fields, such as medicine, human-computer interaction, and customer service. SER has gained significant importance in cognitive psychology due to its potential to enhance user experience, improve patient care, and optimize the interaction between users and products. The process involves the identification and extraction of essential features from speech signals, which are key to accurately recognizing emotional states. The study further explores a range of classification algorithms that are employed to categorize these features, showing a transition from traditional AI techniques, like voice and energy-based analysis, to more advanced deep learning methods. These modern approaches utilize big data and neural network architectures to significantly improve the accuracy, reliability, and robustness of speech emotion recognition systems. Additionally, review articles in this field often draw upon data from SER research, offering valuable insights into the challenges and intricacies involved in the composition and implementation of such systems. The study traces the evolution of SER technology, emphasizing the benefits of deep learning, particularly its ability to learn directly from raw data, as well as the challenges posed by factors such as large log files and the need to accommodate various devices. Comprehensive reviews serve as crucial resources for researchers, practitioners, and policymakers, enabling a deeper understanding of the current state of SER technologies, including their strengths, limitations, and areas for future development. These insights are pivotal in driving the field forward, facilitating the development of innovative applications, and maximizing the potential of SER technologies in real-world scenarios.

Keywords: Feature extraction, machine learning, classification algorithm, natural language processing, speech emotion recognition

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Citation:

How to cite this article: Akshay Shetye, Shruti Chavan, Akshay Parab, Kaushik Patil, and Sumitra Kulkarni, Face and Speech Emotion Recognition. International Journal of Image Processing and Pattern Recognition. 2024; 10(02): 35-43p.

How to cite this URL: Akshay Shetye, Shruti Chavan, Akshay Parab, Kaushik Patil, and Sumitra Kulkarni, Face and Speech Emotion Recognition. International Journal of Image Processing and Pattern Recognition. 2024; 10(02): 35-43p. Available from:https://journalspub.com/publication/ijippr/article=11111
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