Human emotion recognition from speech is studied frequently for its importance in many applications, e.g. human-computer
interaction. There is a wide diversity and non-agreement about the basic emotion or emotion-related states on
one hand and about where the emotion related information lies in the speech signal on the other side. These diversities
motivate our investigations into extracting Meta-features using the PCA approach, or using a non-adaptive random
projection RP, which significantly reduce the large dimensional speech feature vectors that may contain a wide range of
emotion related information. Subsets of Meta-features are fused to increase the performance of the recognition model
that adopts the score-based LDC classifier. We shall demonstrate that our scheme outperform the state of the art results
when tested on non-prompted databases or acted databases (i.e. when subjects act specific emotions while uttering a
sentence). However, the huge gap between accuracy rates achieved on the different types of datasets of speech raises
questions about the way emotions modulate the speech. In particular we shall argue that emotion recognition from
speech should not be dealt with as a classification problem. We shall demonstrate the presence of a spectrum of different
emotions in the same speech portion especially in the non-prompted data sets, which tends to be more “natural” than the
acted datasets where the subjects attempt to suppress all but one emotion.
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