Speech Emotion Detection.
There is a large amount of calls stored every day in call centers . The centers need to make decisions as to which calls require subsequent attention to guarantee the customer requests have been adequately answered and that all issues are cleared. Usually , quality assurance personnel listen to a random selection of calls to tag the calls that require subsequent actions this is both costly, time consuming and might cause important calls to be missed and hence possible loss of customers.
The solution being developed aims to automatically extract angry emotions voice features from historic recorded calls and use the trained model to classify new calls and automatically tag all calls containing angry voice features.
Another benefit of this approach is that it does not depend on the language of the conversation , which make it more suitable and adaptive for different languages as it uses voice features of angry speakers to train the model.
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