  Time-continuous emotion prediction has become an increasingly compelling task
in machine learning. Considerable efforts have been made to advance the
performance of these systems. Nonetheless, the main focus has been the
development of more sophisticated models and the incorporation of different
expressive modalities (e. g., speech, face, and physiology). In this paper,
motivated by the benefit of difficulty awareness in a human learning procedure,
we propose a novel machine learning framework, namely, Dynamic Difficulty
Awareness Training (DDAT), which sheds fresh light on the research -- directly
exploiting the difficulties in learning to boost the machine learning process.
The DDAT framework consists of two stages: information retrieval and
information exploitation. In the first stage, we make use of the reconstruction
error of input features or the annotation uncertainty to estimate the
difficulty of learning specific information. The obtained difficulty level is
then used in tandem with original features to update the model input in a
second learning stage with the expectation that the model can learn to focus on
high difficulty regions of the learning process. We perform extensive
experiments on a benchmark database (RECOLA) to evaluate the effectiveness of
the proposed framework. The experimental results show that our approach
outperforms related baselines as well as other well-established time-continuous
emotion prediction systems, which suggests that dynamically integrating the
difficulty information for neural networks can help enhance the learning
process.
