  During the last decade, the applications of signal processing have
drastically improved with deep learning. However areas of affecting computing
such as emotional speech synthesis or emotion recognition from spoken language
remains challenging. In this paper, we investigate the use of a neural
Automatic Speech Recognition (ASR) as a feature extractor for emotion
recognition. We show that these features outperform the eGeMAPS feature set to
predict the valence and arousal emotional dimensions, which means that the
audio-to-text mapping learning by the ASR system contain information related to
the emotional dimensions in spontaneous speech. We also examine the
relationship between first layers (closer to speech) and last layers (closer to
text) of the ASR and valence/arousal.
