The ability to classify emotions from speech is beneficial in a number of domains, including the study of human relationships. However, manual classification of emotions from speech is time consuming. Current technology supports the automatic classification of emotions from speech, but these systems have some limitations. In particular, existing systems are trained with a given data set and cannot adapt to new data nor can they adapt to different users’ notions of emotions. In this study, we introduce WISE, a web-based interactive speech emotion classification system. WISE has a web-based interface that allows users to upload speech data and automatically classify the emotions within this speech using pre-trained models. The user can then adjust the emotion label if the system classification of the emotion does not agree with the user’s perception, and this updated label is then fed back into the system to retrain the models. In this way, WISE enables the emotion classification models to be adapted over time. We evaluate WISE by simulating the user interactions with the system using the LDC dataset, which has known, ground-truth labels. We evaluate the benefit of the user feedback enabled by WISE in situations where manually classifying emotions in a large dataset is costly, yet trained models alone will not be able to accurately classify the data.<\/p>\n","protected":false},"excerpt":{"rendered":"
The ability to classify emotions from speech is beneficial in a number of domains, including the study of human relationships. However, manual classification of emotions from speech is time consuming. Current technology supports the automatic classification of emotions from speech, but these systems have some limitations. In particular, existing systems are trained with a given 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