  Neural Machine Translation (NMT) is the task of translating a text from one
language to another with the use of a trained neural network. Several existing
works aim at incorporating external information into NMT models to improve or
control predicted translations (e.g. sentiment, politeness, gender). In this
work, we propose to improve translation quality by adding another external
source of information: the automatically recognized emotion in the voice. This
work is motivated by the assumption that each emotion is associated with a
specific lexicon that can overlap between emotions. Our proposed method follows
a two-stage procedure. At first, we select a state-of-the-art Speech Emotion
Recognition (SER) model to predict dimensional emotion values from all input
audio in the dataset. Then, we use these predicted emotions as source tokens
added at the beginning of input texts to train our NMT model. We show that
integrating emotion information, especially arousal, into NMT systems leads to
better translations.
