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.

Authors: Mohíno-Herranz, Inma; Llerena-Aguilar, Sr., Cosme; García-Gómez, Joaquín; Utrilla-Manso, Manuel; Rosa-Zurera, Manuel
Affiliation: University of Alcalá, Alcalá de Henares, Madrid, Spain
AES Convention: 145 (October 2018) Paper Number: 10090
Publication Date: October 7, 2018
Subject: Applications in Audio

Click to purchase paper as a non-member or you can login as an AES member to see more options.

No AES members have commented on this paper yet.

Subscribe to this discussion

To be notified of new comments on this paper you can subscribe to this RSS feed. Forum users should login to see additional options.

Start a discussion!

If you would like to start a discussion about this paper and are an AES member then you can login here:
Username:
Password:

If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.