A comparative study of different weighting schemes on KNN-based emotion recognition in mandarin speech

Abstract

Emotion is fundamental to human experience influencing cognition, perception and everyday tasks such as learning, communication and even rational decision-making. This aspect must be considered in human-computer interaction. In this paper, we compare four different weighting functions in weighted KNN-based classifiers to recognize five emotions, including anger, happiness, sadness, neutral and boredom, from Mandarin emotional speech. The classifiers studied include weighted KNN, weighted CAP, and weighted D-KNN. To give a baseline performance measure, we also adopt traditional KNN classifier. The experimental results show that the used Fibonacci weighting function outperforms than others in all weighted classifiers. The highest accuracy achieves 81.4% with weighted D-KNN classifier. © Springer-Verlag Berlin Heidelberg 2007.