Speech Emotion Recognition Based on Sparse Representation - YAN, Jingjie; WANG, Xiaolan; GU, Weiyi; MA, LiLi (Archives of Acoustics, 2013)
Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do-mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigatespeech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth.We make use of the sparse partial least squares regression method to implement the feature selectionand dimensionality reduction on the whole acquired speech emotion features. By the means of exploitingthe SPLSR method, the component parts of those redundant and meaningless speech emotion featuresare lessened to zero while those serviceable and informative speech emotion features are maintained andselected to the following classification step. A number of tests on Berlin database reveal that the recogni-tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionalityreduction methods.