AN INCREMENTAL ANALYSIS OF DIFFERENT FEATURE GROUPS IN SPEAKER INDEPENDENT EMOTION RECOGNITION (2007)

This paper investigates the classification of different emotional states using speech features from different feature groups. We use both suprasegmental feature groups like pitch, energy, and duration and segmental feature groups like voice quality, zero crossing rate, and articulation. We want to exploit the selection of the most relevant features from these different feature groups to get a better understanding of the speaker independent emotion recognition. We study how these different feature groups overlap or complement each other. By using the sequential floating forward selection algorithm (SFFS), feature subsets maximizing the classification rate will be generated. For this purpose, we use a Bayesian classifier and a speaker independent cross validation. A detailed study is also done on the relevance of the feature groups for classifying different emotion dimensions known from the psychological emotion research. 1. Introduction There are many applications of paralinguistic properties in the literature. One well known application is the detection of emotions from the recorded speech signal [1]. Various attempts show quite good results in the case of speaker dependent classification [2], [3]. By generating an emotion model for every speaker in the database, the utterances of a speaker are classified by using his own emotion model. But the performance of many approaches is poor in the case of speaker independent classification. Speaker independent means that the speaker of the classified utterances is not included in the training database. He is unknown for the classifier and the deduced learning rules. In this paper we follow two goals. One is to improve the classification performance of speaker independent emotion recognition by combining conventional prosodic features with additional segmental features. The second one is to get a better understanding of emotion discrimination by analyzing the role and the interaction of different feature groups in emotion recognition. The paper is organized as follows: The acoustic features extracted for the emotion detection and their grouping are described in the 2. section. In the 3. section, different strategies for an optimum feature composition are discussed and corresponding results of emotion classification are presented. Results for classifying the psychological emotion dimensions can be found in the 4. section. Finally, some conclusions are drawn. 2. Features In emotion recognition, mainly suprasegmental prosodic features have been used up to now. In our approach, the common prosodic features are combined with additional segmental features describing the zero crossing rate, articulation, and the voice quality. We extracted a total number of 216 speech features belonging to different feature groups. Below these feature groups are briefly described. 2.1. Suprasegmental features There are three main classes of prosodic features: pitch, energy, and duration (dur). The features are obtained by measuring statistical values that describe the corresponding feature contours. Mean, median, minimum, maximum, range, variance, and the first two derivatives are calculated from the contours. Pitch features are extracted from the intonation contour of a spoken utterance. The raw value of the pitch is calculated for each analysis segment by using the RAPT algorithm which uses normalized cross correlation and dynamic programming. Energy features are derived from the signal energy contour. Duration features count the number of uninterrupted analysis segments of the same voicing type. In all prosodic features, we distinguish between voiced, unvoiced and pause segments. All together there are 146 prosodic features.