A Novel Classifier Based on Enhanced Lipschitz Embedding for Speech Emotion Recognition

摘要

The paper proposes a novel classifier named ELEC (Enhanced Lipschitz Embedding based Classifier) in the speech emotion recognition system. ELEC adopts geodesic distance to preserve the intrinsic geometry of speech corpus and embeds the high dimensional feature vector into a lower space. Through analyzing the class labels of the neighbor training vectors in the compressed space, ELEC classifies the test data into six archetypal emotional states, i.e. neutral, anger, fear, happiness, sadness and surprise. Experimental results on a benchmark data set demonstrate that compared with the traditional methods, the proposed classifier of ELEC achieves 17% improvement in average for speaker-independent emotion recognition and 13% for speaker-dependent.