Speech Emotion Recognition using a backward context

Abstract

The classification of emotions, such as joy, anger, anxiety, etc. from tonal variations in human speech is an important task for research and applications in human computer interaction. In the preceding work, it has been demonstrated that the locally extracted features of speech match or surpass the performance of global features that has been adopted in current approaches. In this continuing research, a backward context, which also can be considered as a feature vector memory, is shown to improve the prediction accuracy of the Speech Emotion Recognition engine. Preliminary results on German emotional speech database illustrate significant improvements over results from the previous study.