International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Speech Emotion Recognition

Author(s) Yash Dixit, Sakshi Chauhan, Suraj yadav, Shivam Singh, Surabhi
Country India
Abstract Speech emotion recognition is a vital area of research with applications starting from human-computer interaction to mental health monitoring. This paper will provide a comprehensive survey of the techniques, methods, applications, and challenges in speech emotion recognition. It begins by checking the significance of recognizing emotions from speech and its diverse applications across various fields. The paper then shows the method employed for emotion speech recognition, encompassing traditional machine learning techniques, such as support vector machines and Gaussian mixture models, as well as contemporary approaches, including deep learning and multimodal fusion. Moreover, it examines benchmark datasets commonly used for training and evaluation purposes in emotion speech recognition research. Speech Emotion Recognition (SER) has a wide range of applications and there has been a lot
of research going on in this fascinating area in recent years. However, the entertainment sector suffers from a lack of study in this research. Many of use Neural Network (NN) and Long Short-Term Memory (LSTM) architectures to categorize the emotions in audio recordings captured by actors expressing
various emotions. Moreover, our survey explores real-world applications of emotion speech recognition like virtual assistants, health sector, market sector, education, Mental health diagnosis. The paper discusses the challenge associated with emotion speech recognition, including the variability of emotional
expressions, cultural influences, humor and privacy concerns. In future it will help others to deal with many Noisy dataset and various cultural effects of emotion.
Keywords Recognition, speech , Sequential,Lstm
Field Engineering
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-26
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.21105
Short DOI https://doi.org/gtwmp6
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