A Multimodal Approach to Improve Performance Evaluation of Call Center Agent

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dc.contributor.authorAhmed , Abdelrahman
dc.contributor.authorShaalan, Khaled
dc.contributor.authorToral, Sergio
dc.contributor.authorHifny, Yasser
dc.date.accessioned2025-05-14T12:46:50Z
dc.date.available2025-05-14T12:46:50Z
dc.date.issued2021
dc.description.abstractThe paper proposes three modeling techniques to improve the performance evaluation of the call center agent. The first technique is speech processing supported by an attention layer for the agent’s recorded calls. The speech comprises 65 features for the ultimate determination of the context of the call using the Open-Smile toolkit. The second technique uses the Max Weights Similarity (MWS) approach instead of the Softmax function in the attention layer to improve the classification accuracy. MWS function replaces the Softmax function for fine-tuning the output of the attention layer for processing text. It is formed by determining the similarity in the distance of input weights of the attention layer to the weights of the max vectors. The third technique combines the agent’s recorded call speech with the corresponding transcribed text for binary classification. The speech modeling and text modeling are based on combinations of the Convolutional Neural Networks (CNNs) and Bi-directional Long-Short Term Memory (BiLSTMs). In this paper, the classification results for each model (text versus speech) are proposed and compared with the multimodal approach’s results. The multimodal classification provided an improvement of (0.22%) compared with acoustic model and (1.7%) compared with text model.
dc.identifier.citationAhmed, A. et al. (2021) “A Multimodal Approach to Improve Performance Evaluation of Call Center Agent,” Sensors (Basel, Switzerland), 21(8).
dc.identifier.doihttps://doi.org/10.3390/s21082720.
dc.identifier.issn1424-8220, 1424-8220
dc.identifier.urihttps://bspace.buid.ac.ae/handle/1234/3034
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofseriesSensors (Basel, Switzerland)v21 n8 (20210412)
dc.subjectperformance modeling; multimodal classification; BiLSTM; CNNs; attention layer
dc.titleA Multimodal Approach to Improve Performance Evaluation of Call Center Agent
dc.typeArticle
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