Emotion recognition using semi-supervised feature selection with speaker normalization

Abstract

Feature selection methods are the mostly used dimensional reduction methods in speech emotion recognition. However, most methods cannot preserve the manifold of data and cannot use the information provided by unlabeled data, so that they cannot select a good sub feature set for speech emotion recognition. This paper presents a semi-supervised feature selection method that can preserve the manifold structure of data, preserve the category structure, and use the information provided by the unlabeled data. To further deal with the manifold of speech data influenced by factors such as emotion, speaker and sentence, a new speaker normalization method is also proposed, which can achieve a good speaker normalization result in the case of a small number of samples of a speaker available. This speaker normalization method can be used in most real application of speech emotion recognition. The conducted experiments validate the proposed semi-supervised feature selection method with the speaker normalization in terms of the effectiveness of the speech emotion recognition.