Shaila Pawar , Pratiksha Atkari , Atul Lamkhade

One of the quickest and most natural ways for humans to communicate is through speech. Speech emotion recognition is the process of accurately anticipating a human’s emotion from their speech. It improves the way people and computers communicate. Although it is tricky to annotate audio and difficult to forecast a person’s sentiment because emotions are subjective, “Speech Emotion Recognition (SER)” makes this possible. Various researchers have created a variety of systems to extract emotions from the speech stream. Speech qualities in particular are more helpful in identifying between various emotions, and if they are unclear, this is the cause of how challenging it is to identify an emotion from a speaker’s speech. A variety of datasets for speech emotions, its modeling, and types are accessible, and they aid in determining the style of speech. After feature extraction, the classification of speech emotions is a crucial component, so in this system proposal, we introduced Artificial Neural Networks (ANN model) that are utilized to distinguish emotions such as angry, disgust, Fear, happy, neutral, Sad and surprise. The proposed system model ArtifIicial Neural Networks (ANN model) achieved training accuracy of 100% and Validation accuracy of 99%.

https://doi.org/10.58357/jedcs.2024.v3.i2.8