Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation |
Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation | python
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This paper proposes an emotion recognition system based on speech signals an algorithmic approach for detection of human emotions with the help of speech. The prime objective of this paper is to recognize emotions in speech and classify them in 5 emotion output classes namely angry, fear, enthusiast, happy, sad.Emotion in speech carries extra insight about human actions. Human speech conveys information and context through speech, tone, pitch and many such characteristics of the human vocal system. As human machine interactions evolve, there is a need to buttress the outcomes of such interactions by equipping the computer and machine interfaces with the ability to recognize the emotion of the speaker. Emotions play a vital role in human communication. In order to extend its role towards the human-machine interaction, itis desirable for the computers to have some built-in abilities for recognizing the different emotional states of the user .Today, a large amount of resources and efforts are being put into the development of artificial intelligence, and smart machines, all for the primary purpose of simplifying human life. Research studies have provided evidence that human emotions influence the decision making process to a certain extent. If the machine is able to recognize the underlying emotion in human speech, it will result in both constructive response and communication. CNN for Emotion prediction. We report accuracy, f-score, precision and recall for the different experiment settings we evaluated our models in. KNN was found to have the highest accuracy and predicted correct emotion 88.21% of the time.