Evaluation of Speech Emotion Classification Based on GMM and Data Fusion

Abstract

This paper describes continuation of our research on automatic emotion recognition from speech based on Gaussian Mixture Models (GMM). We use similar technique for emotion recognition as for speaker recognition. From previous research it seems to be better to use a lesser number of GMM components than is used for speaker recognition and better results are also achieved for a greater number of speech parameters used for GMM modeling. In previous experiments we used suprasegmental and segmental parameters separately and also together, which can be described as fusion on feature level. The experiment described in this paper is based on an evaluation of score level fusion for two GMM classifiers used separately for segmental and suprasegmental parameters. We evaluate two techniques of score level fusion – dot product of scores from both classifiers and maximum selection and maximum confidence selections.KeywordsGaussian Mixture ModelEmotion RecognitionSpeaker RecognitionLevel FusionRecognition ScoreThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.