Automatic emotional speech classification

Abstract

Our purpose is to design a useful tool which can be used in psychology to automatically classify utterances into five emotional states such as anger, happiness, neutral, sadness, and surprise. The major contribution of the paper is to rate the discriminating capability of a set of features for emotional speech recognition. A total of 87 features has been calculated over 500 utterances from the Danish Emotional Speech database. The Sequential Forward Selection method (SFS) has been used in order to discover a set of 5 to 10 features which are able to classify the utterances in the best way. The criterion used in SFS is the crossvalidated correct classification score of one of the following classifiers: nearest mean and Bayes classifier where class pdfs are approximated via Parzen windows or modelled as Gaussians. After selecting the 5 best features, we reduce the dimensionality to two by applying principal component analysis. The result is a 51.6% ± 3% correct classification rate at 95% confidence interval for the five aforementioned emotions, whereas a random classification would give a correct classification rate of 20%. Furthermore, we find out those twoclass emotion recognition problems whose error rates contribute heavily to the average error and we indicate that a possible reduction of the error rates reported in this paper would be achieved by employing two-class classifiers and combining them.

Gesture-Based Affective Computing on Motion Capture Data

“…Currently there are two main approaches to affective computing: Audio-based techniques to determine emotion from spoken word are described for example in [4,5,6] and video-based techniques that examine and classify facial expressions are described in [7,8,9]. More advanced systems are multi-modal and use a variety of microphones, video cameras as well as other sensors to enlighten the machine with richer signals from the human [10,11,12].…”

On the Variants of the Self-Organizing Map That Are Based on Order Statistics

“…The Student t-test for unequal variances has also found that the differences in the average assignment ratio per emotion are statistically significant for a 15-fold cross validation experiment. Figure 1 depicts a partition of the 2D feature domain that has been resulted after selecting the five best emotional features by the Sequential Forward Selection algorithm and applying Principal Component Analysis in order to reduce the dimensionality from five dimensions (5D) to two dimensions (2D) [16]. Only the samples which belong to the interquartile range of the probability density function for each class are shown.…”

“…A number of 1160 emotional speech patterns are extracted. Each pattern consists of a 90-dimensional feature vector [16]. Each emotional pattern is classified into one of the five primitive emotional states, such as hot anger, happiness, neutral, sadness, and surprise.…”

A Case Study of Speech Emotion Recognition.

“…To obtain the statistics of energy feature, we use short-term function to extract the value of energy in each speech frame. Then we can obtain the statistics of energy in the whole speech sample by calculating the energy, such as mean value, max value, variance, variation range, contour of energy [6].…”