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Predicting continuous emotion in terms of affective attrib-utes has mainly been focused on hard labels, which ignored the ambiguity of recognizing certain emotions. This ambigu-ity may result in high inter-rater variability and in turn caus-es varying prediction uncertainty with time. Based on the assumption that temporal dependencies occur in the evolu-tion of emotion uncertainty, this paper proposes a dynamic multi-rater Gaussian Mixture Regression (GMR), aiming to obtain the emotion uncertainty prediction reflected by multi-raters by taking into account their temporal dependencies.

In this paper we focus on the usefulness of verbal events for speech based emotion recognition. In particular, the use of phoneme sequences to encode verbal cues related to the expression of emotions is proposed and lexical features based on these phoneme sequences are introduced for use in automatic emotion recognition systems where manual transcripts are not available. Secondly, a novel estimate of emotional salience of verbal cues, applicable to both phoneme sequences and words, is presented.