Performance Evaluation of Call Center Agents by Neural Networks

2016 ◽  
Author(s):  
Hui-Huang Hsu ◽  
Te-Chan Chen ◽  
Wei-Tsung Chan ◽  
Jung-Kuei Chang
Keyword(s):  
Neural Networks ◽  
Performance Evaluation ◽  
Call Center

Performance Evaluation of Single-layered Neural Networks

1993 ◽  
pp. 219-257
Author(s):  
N. B. Karayiannis ◽  
A. N. Venetsanopoulos
Keyword(s):  
Neural Networks ◽  
Performance Evaluation

scholarly journals Acoustic feature-based sentiment analysis of call center data

2017 ◽  
Author(s):  
◽  
Zeshan Peng
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Emotion Recognition ◽  
Sentiment Analysis ◽  
Call Center ◽  
Machine Learning Algorithms ◽  
Language Recognition ◽  
Acoustic Features ◽  
Learning Methods ◽  
Machine Learning Methods

With the advancement of machine learning methods, audio sentiment analysis has become an active research area in recent years. For example, business organizations are interested in persuasion tactics from vocal cues and acoustic measures in speech. A typical approach is to find a set of acoustic features from audio data that can indicate or predict a customer's attitude, opinion, or emotion state. For audio signals, acoustic features have been widely used in many machine learning applications, such as music classification, language recognition, emotion recognition, and so on. For emotion recognition, previous work shows that pitch and speech rate features are important features. This thesis work focuses on determining sentiment from call center audio records, each containing a conversation between a sales representative and a customer. The sentiment of an audio record is considered positive if the conversation ended with an appointment being made, and is negative otherwise. In this project, a data processing and machine learning pipeline for this problem has been developed. It consists of three major steps: 1) an audio record is split into segments by speaker turns; 2) acoustic features are extracted from each segment; and 3) classification models are trained on the acoustic features to predict sentiment. Different set of features have been used and different machine learning methods, including classical machine learning algorithms and deep neural networks, have been implemented in the pipeline. In our deep neural network method, the feature vectors of audio segments are stacked in temporal order into a feature matrix, which is fed into deep convolution neural networks as input. Experimental results based on real data shows that acoustic features, such as Mel frequency cepstral coefficients, timbre and Chroma features, are good indicators for sentiment. Temporal information in an audio record can be captured by deep convolutional neural networks for improved prediction accuracy.

Performance evaluation of whey flux in dead-end and cross-flow modes via convolutional neural networks

2022 ◽  
Vol 301 ◽  
pp. 113872
Author(s):  
Lukka Thuyavan Yogarathinam ◽  
Kirubakaran Velswamy ◽  
Arthanareeswaran Gangasalam ◽  
Ahmad Fauzi Ismail ◽  
Pei Sean Goh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Performance Evaluation ◽  
Convolutional Neural Networks ◽  
Cross Flow ◽  
Dead End

scholarly journals Performance evaluation of coherent Ising machines against classical neural networks

2017 ◽  
Vol 2 (4) ◽  
pp. 044002 ◽  
Author(s):  
Yoshitaka Haribara ◽  
Hitoshi Ishikawa ◽  
Shoko Utsunomiya ◽  
Kazuyuki Aihara ◽  
Yoshihisa Yamamoto
Keyword(s):  
Neural Networks ◽  
Performance Evaluation

scholarly journals Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5489
Author(s):  
Abdelrahman Ahmed ◽  
Sergio Toral ◽  
Khaled Shaalan ◽  
Yaser Hifny
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Call Center ◽  
Short Term Memory ◽  
Call Centers ◽  
Binary Classification ◽  
Speech Signal Processing ◽  
Baseline System ◽  
Long Short Term Memory ◽  
Current Systems

Measuring the productivity of an agent in a call center domain is a challenging task. Subjective measures are commonly used for evaluation in the current systems. In this paper, we propose an objective framework for modeling agent productivity for real estate call centers based on speech signal processing. The problem is formulated as a binary classification task using deep learning methods. We explore several designs for the classifier based on convolutional neural networks (CNNs), long-short-term memory networks (LSTMs), and an attention layer. The corpus consists of seven hours collected and annotated from three different call centers. The result shows that the speech-based approach can lead to significant improvements (1.57% absolute improvements) over a robust text baseline system.

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