  Emotion is an inherently subjective psychophysiological human-state and to
produce an agreed-upon representation (gold standard) for continuous emotion
requires a time-consuming and costly training procedure of multiple human
annotators. There is strong evidence in the literature that physiological
signals are sufficient objective markers for states of emotion, particularly
arousal. In this contribution, we utilise a dataset which includes continuous
emotion and physiological signals - Heartbeats per Minute (BPM), Electrodermal
Activity (EDA), and Respiration-rate - captured during a stress inducing
scenario (Trier Social Stress Test). We utilise a Long Short-Term Memory,
Recurrent Neural Network to explore the benefit of fusing these physiological
signals with arousal as the target, learning from various audio, video, and
textual based features. We utilise the state-of-the-art MuSe-Toolbox to
consider both annotation delay and inter-rater agreement weighting when fusing
the target signals. An improvement in Concordance Correlation Coefficient (CCC)
is seen across features sets when fusing EDA with arousal, compared to the
arousal only gold standard results. Additionally, BERT-based textual features'
results improved for arousal plus all physiological signals, obtaining up to
.3344 CCC compared to .2118 CCC for arousal only. Multimodal fusion also
improves overall CCC with audio plus video features obtaining up to .6157 CCC
to recognize arousal plus EDA and BPM.
