Linguistic based emotion analysis using softmax over time attention mechanism.

Recognizing the real emotion of humans is considered the most essential task for any customer feedback or medical applications. There are many methods available to recognize the type of emotion from speech signal by extracting frequency, pitch, and other dominant features. These features are used to train various models to auto-detect various human emotions. We cannot completely rely on the features of speech signals to detect the emotion, for instance, a customer is angry but still, he is speaking at a low voice (frequency components) which will eventually lead to wrong predictions. Even a video-based emotion detection system can be fooled by false facial expressions for various emotions. To rectify this issue, we need to make a parallel model that will train on textual data and make predictions based on the words present in the text. The model will then classify the type of emotions using more comprehensive information, thus making it a more robust model. To address this issue, we have tested four text-based classification models to classify the emotions of a customer. We examined the text-based models and compared their results which showed that the modified Encoder decoder model with attention mechanism trained on textual data achieved an accuracy of 93.5%. This research highlights the pressing need for more robust emotion recognition systems and underscores the potential of transfer models with attention mechanisms to significantly improve feedback management processes and the medical applications.

The children's emotional speech recognition by adults: Cross-cultural study on Russian and Tamil language.

The current study investigated the features of cross-cultural recognition of four basic emotions "joy-neutral (calm state)-sad-anger" in the spontaneous and acting speech of Indian and Russian children aged 8-12 years across Russian and Tamil languages. The research tasks were to examine the ability of Russian and Indian experts to recognize the state of Russian and Indian children by their speech, determine the acoustic features of correctly recognized speech samples, and specify the influence of the expert's language on the cross-cultural recognition of the emotional states of children. The study includes a perceptual auditory study by listeners and instrumental spectrographic analysis of child speech. Different accuracy and agreement between Russian and Indian experts were shown in recognizing the emotional states of Indian and Russian children by their speech, with more accurate recognition of the emotional state of children in their native language, in acting speech vs spontaneous speech. Both groups of experts recognize the state of anger via acting speech with the high agreement. The difference between the groups of experts was in the definition of joy, sadness, and neutral states depending on the test material with a different agreement. Speech signals with emphasized differences in acoustic patterns were more accurately classified by experts as belonging to emotions of different activation. The data showed that, despite the universality of basic emotions, on the one hand, the cultural environment affects their expression and perception, on the other hand, there are universal non-linguistic acoustic features of the voice that allow us to identify emotions via speech.