Weighted Ordered Classes - Nearest Neighbors : A New Framework for Automatic Emotion Recognition From Speech

摘要

In this paper we present a new framework for emotion recognition from speech based on a similarity concept called Weighted Ordered Classes-Nearest Neighbors (WOC-NN). Unlike the k-nearest neighbor, an instance-similarity based method; WOC-NN computes similarities between a test instance and a class pattern of each emotion class. An emotion class pattern is a representation of its ranked neighboring classes. A Hamming distance is used as distance metric, enhanced with two improvements: i) weighting the importance of each class rank of each neighborhood pattern and ii) discarding irrelevant class ranks from the patterns. Thus, the decision process in WOC-NN exploits more information than Bayes rule which makes use only of the information about the model class that minimizes Bayes risk. This extra information allows WOC-NN to get more accurate prediction. Also, the results show that the proposed system outperforms the result of state-of-the art systems when applied to the FAU AIBO corpus.