Speech Emotion Recognition Using Canonical Correlation Analysis and Probabilistic Neural Network

In this paper, automatic identification of emotional states from human speech is addressed. While several papers have been published in the literature on speech emotion recognition, the features used are taken or modified from those used for speech recognition purposes. However, not all features used for speech recognition are of equal importance for emotion recognition. This paper addresses this issue and proposes a systematic method on feature selection for emotion recognition from speech signals. The idea is to work on a well-selected small feature set and use it to remove irrelevant information. Specifically, the proposed method uses the similar idea of the Canonical Correlation Analysis (CCA) to estimate the linear relationship between the various features and the emotional states. The outcome is a set of features that are of most relevance to the emotions. Experiments have been conducted using the LDC database and with the use of the Probabilistic Neural Network (PNN) as the classification method. The results obtained show that, comparable accuracies can be obtained for the emotional states tested with the use of only about 30% of the features considered. This implies that the computational load can be reduced greatly too.