@conference{galanisCoginfo,
author = "Galanis, Dimitris and Karabetsos, Sotiris and Koutsombogera, Maria and Papageorgiou, Harris and Anna Esposito and Maria Teresa Riviello",
abstract = "Detecting emotional traits in call centre 
interactions can be beneficial to the quality management of the 
services provided, since this reveals the positioning of both 
speakers, i.e. satisfaction or frustration and anger on the 
customers’ side, and stress detection, disappointment mitigation 
or failure to provide the requested service on the operators’ side. 
This paper describes a machine learning approach to classify 
emotional speech units occurring in a call centre dataset by 
employing emotion-related labels, automatically extracted 
acoustic features as well as additional context-related features.",
booktitle = "Cognitive Infocommunications (CogInfoCom), 2013 IEEE 4th International Conference",
doi = "10.1109/CogInfoCom.2013.6719279",
isbn = "978-1-4799-1543-9",
keywords = "emotion classification, emotional speech, call centre  interactions, SVM",
month = "December",
pages = "403-406",
publisher = "IEEE",
title = "{C}lassification of emotional speech units in call centre interactions ",
url = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp={\&}arnumber=6719279{\&}tag=1",
year = "2013",
}