Speech Emotion Recognition for Affective Human-Robot Interaction

摘要

We evaluate the performance of a speech emotion recognition method for affective human-robot interaction. In the proposed method, emotion is classified into 6 classes: Angry, bored, happy, neutral, sad, and surprised. After applying noise reduction and speech detection, we obtain a feature vector for an utterance from statistics of phonetic and prosodic information. The phonetic information includes log energy, shimmer, formant frequencies, and Teager energy; the prosodic information includes pitch, jitter, and rate of speech. Then a pattern classifier based on Gaussian support vector machines decides the emotion class of the utterance. To simulate a human-robot interaction situation, we record speech commands and dialogs uttered at 2m away from a microphone. Experimental results show that the proposed method achieves the classification accuracy of 58.6 % while listeners give 60.4 % with the reference labels given by speakers’ intention. On the other hand, the proposed method shows the classification accuracy of 51.2 % with the reference labels given by the listeners ’ majority decision. 1.