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I. Mohíno-Herranz, C. Llerena-Aguilar, J. García-Gómez, M. Utrilla-Manso, M. Rosa-Zurera, "Precision Maximization in Anger Detection in Interactive Voice Response Systems," Paper 10090, (2018 October.). doi: I. Mohíno-Herranz, C. Llerena-Aguilar, J. García-Gómez, M. Utrilla-Manso, M. Rosa-Zurera, "Precision Maximization in Anger Detection in Interactive Voice Response Systems," Paper 10090, (2018 October.). doi:
Abstract: Detection is usually carried out following the Neyman-Pearson criterion to maximize the probability of detection (true positives rate), maintaining the probability of false alarm (false positives rate) below a given threshold. When the classes are unbalanced, the performance cannot be measured just in terms of true positives and false positives rates, and new metrics must be introduced, such as Precision. “Anger detection” in Interactive Voice Response (IVR) systems is one application where precision is important. In this paper a cost function for features selection to maximize precision in anger detection applications is presented. The method has been proved with a real database obtained by recording calls managed by an IVR system, demonstrating its suitability.

@article{mohíno-herranz2018precision,
author={mohíno-herranz, inma and llerena-aguilar, sr., cosme and garcía-gómez, joaquín and utrilla-manso, manuel and rosa-zurera, manuel},
journal={journal of the audio engineering society},
title={precision maximization in anger detection in interactive voice response systems},
year={2018},
volume={},
number={},
pages={},
doi={},
month={october},} @article{mohíno-herranz2018precision,
author={mohíno-herranz, inma and llerena-aguilar, sr., cosme and garcía-gómez, joaquín and utrilla-manso, manuel and rosa-zurera, manuel},
journal={journal of the audio engineering society},
title={precision maximization in anger detection in interactive voice response systems},
year={2018},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={detection is usually carried out following the neyman-pearson criterion to maximize the probability of detection (true positives rate), maintaining the probability of false alarm (false positives rate) below a given threshold. when the classes are unbalanced, the performance cannot be measured just in terms of true positives and false positives rates, and new metrics must be introduced, such as precision. “anger detection” in interactive voice response (ivr) systems is one application where precision is important. in this paper a cost function for features selection to maximize precision in anger detection applications is presented. the method has been proved with a real database obtained by recording calls managed by an ivr system, demonstrating its suitability.},}

TY - paper
TI - Precision Maximization in Anger Detection in Interactive Voice Response Systems
SP - EP -
AU - Mohíno-Herranz, Inma
AU - Llerena-Aguilar, Sr., Cosme
AU - García-Gómez, Joaquín
AU - Utrilla-Manso, Manuel
AU - Rosa-Zurera, Manuel
PY - 2018
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2018 TY - paper
TI - Precision Maximization in Anger Detection in Interactive Voice Response Systems
SP - EP -
AU - Mohíno-Herranz, Inma
AU - Llerena-Aguilar, Sr., Cosme
AU - García-Gómez, Joaquín
AU - Utrilla-Manso, Manuel
AU - Rosa-Zurera, Manuel
PY - 2018
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2018
AB - Detection is usually carried out following the Neyman-Pearson criterion to maximize the probability of detection (true positives rate), maintaining the probability of false alarm (false positives rate) below a given threshold. When the classes are unbalanced, the performance cannot be measured just in terms of true positives and false positives rates, and new metrics must be introduced, such as Precision. “Anger detection” in Interactive Voice Response (IVR) systems is one application where precision is important. In this paper a cost function for features selection to maximize precision in anger detection applications is presented. The method has been proved with a real database obtained by recording calls managed by an IVR system, demonstrating its suitability.