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SER (Speech Emotion Recognition) is a notably
advancing field. Its primary purpose is the identification of
emotions delivered via speech; it is essential for applications
involving computer and human interaction in fields such as
healthcare, entertainment, and psychology. This research studies
the use of SER in feature extraction implemented with the help of
a library based on Python called Librosa, an application of CNN
(Convolutional Neural Network) and SVM (Support Vector
Machine) for segregating emotions. The issues are resolved by
enhancing the reliability and precision of systems in the
identification of emotions by analyzing the efficiency of CNN as
well as SVM in segregating emotions such as Sadness, Surprise,
Happiness, Neutral, and Anger. The outcomes are that CNN
performs much more efficiently with 89.20% accuracy, whereas
SVM only has about 80.50% accuracy. Consistency is maintained
by CNN, which has higher F1 scores, recall, and precision in all
categories. This proves its ability to deal with the complexities of
segregating emotions delivered via speech. It is divulged through
the confusion matrices that both models can give good
performance while handling certain emotions, but CNN acquires
a higher accuracy with fewer incorrect classifications, especially
while figuring out emotions that have acoustic properties that are
quite similar. The research concluded by stating that CNN is more
suitable for tasks implementing SER because of its ability to
capture detailed patterns of emotions in speech more efficiently
than SVM. Future work may include architectures of deep
learning, which are more advanced, like Transformer-based
models or RNN, and having the dataset expanded so that there is
an increase in generalization of different emotional backgrounds
and expressions. Also, incorporating approaches to reduce noise
and different audio environments would help enhance the model
so that it adapts easily to applications used in the real world,
offering applicable and robust SER systems.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
B. Pavin
Dr.N.V. Chinnasamy