Confidence measures for speech emotion recognition: A start

Speech emotion recognition (SER) in use today lacks the ability to evaluate reliability of recognition results although it has matured to the degree of first applicability. In this paper, we thus propose a novel confidence measure for SER systems. The confidence measure is based on human labeller agreement. This information is used to build a series of emotion scoring models to provide multiple agreement levels for a hypothesised emotion state. A fusion is carried out on multiple agreement levels for a confidence score. Experimental results on the FAU Aibo Emotion Corpus of the INTERSPEECH 2009 Emotion Challenge show that the proposed confidence score has strong correlation with the unweighted average recall of the target task - emotion -, thus effectively indicating the usefulness of the confidence measures.