  This paper investigates the influence of different acoustic features,
audio-events based features and automatic speech translation based lexical
features in complex emotion recognition such as curiosity. Pretrained networks,
namely, AudioSet Net, VoxCeleb Net and Deep Speech Net trained extensively for
different speech based applications are studied for this objective. Information
from deep layers of these networks are considered as descriptors and encoded
into feature vectors. Experimental results on the EmoReact dataset consisting
of 8 complex emotions show the effectiveness, yielding highest F1 score of 0.85
as against the baseline of 0.69 in the literature.
