Real-time voice affect recognition for call center agents
Date of Publication
2012
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Computer Science
College
College of Computer Studies
Department/Unit
Computer Science
Thesis Adviser
Jocelyn W. Cu
Defense Panel Member
Merlin Teodosia C. Suarez
Jocelyn W. Cu
Macario O. Cordel, II
Abstract/Summary
Call centers depend depend on their agents to effectively serve their clients. But often times, arguments between agents and clients occur. Inability to deal with this problem may lead client discontent, business loss or moving of client to other competitors. Thus, it is important for call centers to handle this disputes. This study aims to build a real-time affect recognition system with the use of dimensional labels to determine the agents' human affective states. This will allow agents to evaluate themselves, improve their performance while talking to their clients and at the same time avoid arguments with clients.
A Call Center Affect Recognition System (CARSys) was built in this study. There were two applications developed, namely, CARSys Stand Alone and CARSys Logs. CARSys Stand Alone is the application which predicts affective state in real-time. The representation of affective states is dimensional and the dimensions used was valence and arousal. CARSys Logs, on the other hand, is the application responsible for viewing the logs stored in a server. The results showed that the CARSys needs a lot of improvement in terms of the affect recognition and log generation.
The models used by CARSys achieved a mean absolute error (MAE) of 0.2312 and 0.2028 for the valence and arousal, respectively. Testing using the SEMAINE database obtained an MAE of 0.2980 and o.3893 for the valence and arousal, respectively. Testing using the gathered data from CARSys testing, it gained an MAE of 0.2405 for valence and 0.7827 for arousal.
Abstract Format
html
Language
English
Format
Accession Number
TU18537
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Physical Description
1v. various foliations : illustrations (some colored) ; 28 cm.
Recommended Citation
De Leon, S., Paz, R., Tan, J., & Tan, J. (2012). Real-time voice affect recognition for call center agents. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/11871