Development of prosodic feature set for emotion recognition in call center speech - Ebarvia, Eliza Concepcion E.
For call centers, their ability to provide satisfactory service to clients measures their operational output. As such, customers' emotions should be correctly gauged to attend to the customers' need efficiently. Call center owners also need to keep track of how their agents react to clients. In an attempt to help call center owners and agents, an automated emotion recognition system should be developed. In this project, we developed the feature parameters needed to formulate an emotion recognition algorithm for technical call centers. Four emotions were considered for the system output, namely: anger, boredom, happiness or satisfaction, and neutral for both client and the call center agent. This emotion recognition system should be independent of the context of the words uttered; therefore, only prosodic features were considered as system parameters. Three groups of features - rhythm, pitch contour and voiced parts, with their derivatives and statistical characteristics were extracted from the speech sample as the preliminary feature set. The Mel-Frequency Cepstral Coefficients were extracted as additional parameters in the features set. Each feature was then tested for its base performance using the ration between-class variance and the within-class variance. Multivariate Discriminant analysis was also performed on each feature to determine their success rate. The values of these features and the different test results were used to train the emotion classifiers developed by the researchers from the Department of Computer Science. The results from the base performance test show that the features relating to the individual voiced parts and fundamental frequency achieved high base performance ranking. Most of these features, together with the pitch and intensity contour also achieved high success rate after the discriminant analysis test. Among all features, the Mel-frequency cepstral coefficients reached the highest percentage accuracy of 68.156%. As for the emotions, boredom was the emotion least likely recognized by the discriminant analysis while neutral was the most likely recognized emotion.