  Recognizing a speaker's emotion from their speech can be a key element in
emergency call centers. End-to-end deep learning systems for speech emotion
recognition now achieve equivalent or even better results than conventional
machine learning approaches. In this paper, in order to validate the
performance of our neural network architecture for emotion recognition from
speech, we first trained and tested it on the widely used corpus accessible by
the community, IEMOCAP. We then used the same architecture as the real life
corpus, CEMO, composed of 440 dialogs (2h16m) from 485 speakers. The most
frequent emotions expressed by callers in these real life emergency dialogues
are fear, anger and positive emotions such as relief. In the IEMOCAP general
topic conversations, the most frequent emotions are sadness, anger and
happiness. Using the same end-to-end deep learning architecture, an Unweighted
Accuracy Recall (UA) of 63% is obtained on IEMOCAP and a UA of 45.6% on CEMO,
each with 4 classes. Using only 2 classes (Anger, Neutral), the results for
CEMO are 76.9% UA compared to 81.1% UA for IEMOCAP. We expect that these
encouraging results with CEMO can be improved by combining the audio channel
with the linguistic channel. Real-life emotions are clearly more complex than
acted ones, mainly due to the large diversity of emotional expressions of
speakers. Index Terms-emotion detection, end-to-end deep learning architecture,
call center, real-life database, complex emotions.
