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During the last decade, the applications of signal processing havedrastically improved with deep learning. However areas of affecting computingsuch as emotional speech synthesis or emotion recognition from spoken languageremains challenging. In this paper, we investigate the use of a neuralAutomatic Speech Recognition (ASR) as a feature extractor for emotionrecognition. We show that these features outperform the eGeMAPS feature set topredict the valence and arousal emotional dimensions, which means that theaudio-to-text mapping learning by the ASR system contain information related tothe emotional dimensions in spontaneous speech. We also examine therelationship between first layers (closer to speech) and last layers (closer totext) of the ASR and valence/arousal.

ASR-based Features for Emotion Recognition: A Transfer Learning Approach