International Journal For Multidisciplinary Research

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A Survey on Speech Emotion Recognition System using CNN Algorithm

Author(s) K. Anisha, K. Vamsi Priya, Ch. Sahithi, N. Devi Charan, A.V.S. SivaRamaRao, G. Prasanth Kumar
Country India
Abstract The speech emotion recognition system (SER) plays an important role in decoding and predicting the speaker's emotional state by analyzing audio signals. Emotions are often simplified and grouped into categories such as anger, happiness, sadness, and even neutral emotional states. These emotions serve to communicate one's thoughts and provide insight into one's physical and mental health. Speech is the most basic and natural form of communication when interacting with others. Voice signals enable rapid communication between individuals, making them a valuable and effective method of expression. Over the past decades, countless research initiatives have been devoted to the development of voice-based automatic emotion recognition systems, especially to improve human-machine communication. Voice is gradually taking center stage in the field of Human-Machine interfaces in the IT sector. This interdisciplinary field draws on a variety of fields, including computer science, signal processing, psychology, linguistics, and more. As technology advances, it allows for seamless communication between humans and machines. Speech recognition not only interprets speech but also captures nuances in a person's tone and expressions, similar to body language. Therefore, it becomes an essential element of the Human-Machine communication system.
Keywords Speech Emotion Recognition (SER), CNN Algorithm, and Artificial Intelligence, Dataset.
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-03-15
Cite This A Survey on Speech Emotion Recognition System using CNN Algorithm - K. Anisha, K. Vamsi Priya, Ch. Sahithi, N. Devi Charan, A.V.S. SivaRamaRao, G. Prasanth Kumar - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.14739
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.14739
Short DOI https://doi.org/gtmzvd
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