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Automatic speech emotion recognition (SER) by a computer is a criticalcomponent for more natural human-machine interaction. As in human-humaninteraction, the capability to perceive emotion correctly is essential to takefurther steps in a particular situation. One issue in SER is whether it isnecessary to combine acoustic features with other data such as facialexpressions, text, and motion capture. This research proposes to combineacoustic and text information by applying a late-fusion approach consisting oftwo steps. First, acoustic and text features are trained separately in deeplearning systems. Second, the prediction results from the deep learning systemsare fed into a support vector machine (SVM) to predict the final regressionscore. Furthermore, the task in this research is dimensional emotion modelingbecause it can enable a deeper analysis of affective states. Experimentalresults show that this two-stage, late-fusion approach, obtains higherperformance than that of any one-stage processing, with a linear correlationfrom one-stage to two-stage processing. This late-fusion approach improvesprevious early fusion results measured in concordance correlation coefficientsscore.

Two-stage dimensional emotion recognition by fusing predictions of acoustic and text networks using SVM