  This paper focuses on finding suitable features to robustly recognize
emotions and evaluate customer satisfaction from speech in real acoustic
scenarios. The classification of emotions is based on standard and well-known
corpora and the evaluation of customer satisfaction is based on recordings of
real opinions given by customers about the received service during phone calls
with call-center agents. The feature sets considered in this study include two
speaker models, namely x-vectors and i-vectors, and also the well known feature
set introduced in the Interspeech 2010 Paralinguistics Challenge (I2010PC).
Additionally, we introduce the use of phonation, articulation and prosody
features extracted with the DisVoice framework as alternative feature sets to
robustly model emotions and customer satisfaction from speech. The results
indicate that the I2010PC feature set is the best approach to classify emotions
in the standard databases typically used in the literature. When considering
the recordings collected in the call-center, without any control over the
acoustic conditions, the best results are obtained with our articulation
features. The I2010PC feature set includes 1584 measures while the articulation
approach only includes 488 measures. We think that the proposed approach is
more suitable for real-world applications where the acoustic conditions are not
controlled and also it is potentially more convenient for industrial
applications.
