  Most automatic emotion recognition systems exploit time-continuous
annotations of emotion to provide fine-grained descriptions of spontaneous
expressions as observed in real-life interactions. As emotion is rather
subjective, its annotation is usually performed by several annotators who
provide a trace for a given dimension, i.e. a time-continuous series describing
a dimension such as arousal or valence. However, annotations of the same
expression are rarely consistent between annotators, either in time or in
value, which adds bias and delay in the trace that is used to learn predictive
models of emotion. We therefore propose a method that can dynamically
compensate inconsistencies across annotations and synchronise the traces with
the corresponding acoustic features using Recurrent Neural Networks.
Experimental evaluations were carried on several emotion data sets that include
Chinese, French, German, and Hungarian participants who interacted remotely in
either noise-free conditions or in-the-wild. The results show that our method
can significantly increase inter-annotator agreement, as well as correlation
between traces and audio features, for both arousal and valence. In addition,
improvements are obtained in the automatic prediction of these dimensions using
simple light-weight models, especially for valence in noise-free conditions,
and arousal for recordings captured in-the-wild.
