Abstract. Speech related processing tasks have been commonly tackled using engineered features, also known as hand-crafted descriptors. These features have usually been optimized along years by the research community that constantly seeks for the most meaningful, robust, and compact audio representations for the specific domain or task. In the last years, a great interest has arisen to develop architectures that are able to learn by themselves such features, thus by-passing the required engineering effort. In this work we explore the possibility to use Convolutional Neural Networks (CNN) directly on raw audio signals to automatically learn meaningful features. Additionally, we study how well do the learned features generalize for a different task. First, a CNN-based continuous conflict detector is trained on audios extracted from televised political debates in French. Then, while keeping previous learned features, we adapt the last layers of the network for targeting another concept by using completely unrelated data. Concretely, we predict self-reported customer satisfaction from call center conversations in Spanish. Reported results show that our proposed approach, using raw audio, obtains similar results than those of a CNN using classical Mel-scale filter banks. In addition, the learning transfer from the conflict detection task into satisfaction prediction shows a successful generalization of the learned features by the deep architecture.
A Convolutional Neural Network (CNN) based end-to-end architecture for customer satisfaction prediction from contact center phone calls uses conflict detection as an auxiliary task to initialize the network weights REF .
Automatic Speech Feature Learning for Continuous Prediction of Customer Satisfaction in Contact Center Phone Calls
IberSPEECH
Computer Science