Abstract
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 investigate speech 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 selection and dimensionality
reduction on the whole acquired speech emotion features. By the means of
exploiting the SPLSR method, the component parts of those redundant and
meaningless speech emotion features are lessened to zero while those
serviceable and informative speech emotion features are maintained and
selected 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 dimensionality reduction
methods.
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