Interactive Voice Response field classifiers

Accurate classification of caller interaction within Interactive Voice Response systems would assist corporations to determine caller behaviour within these solutions. This paper proposes an application, which employs artificial neural networks that could assist contact centers to determine caller activity within their automated systems. Multi-layer perceptron and Radial Basis Function neural network architectures are implemented as classifiers to determine caller interaction. Field classifiers for a pay beneficiary application were developed. dasiaSay accountpsila networks were created utilizing dasiageneratedpsila and dasialivepsila data sources. Multi-layer perceptron networks proved appropriate for this application. The most accurate network created, 99.99%, is the dasiaSay accountpsila classifier. The difference in accuracy between the dasiageneratedpsila and dasialivepsila classifiers is approximately 2%. However, greater development effort is required to implement the former. As a result, the dasialivepsila data source methodology is preferred.