Fast call-classification system development without in-domain training data

This paper presents a new method for the fast development of call-routing systems based on pre-existing corpora and knowledge databases. This method pushes forward the reduction of specific data collection and annotation for developing a new call-classification system. No specific data collection is needed for training both for the Automatic Speech Recognition (ASR) and classification models. The main idea is to re-use existing data to train the models, according to a priori knowledge on the task targeted. The experimental framework used in this study is a call-routing system applied to a civil service information telephone application. All the a priori knowledge used to develop the system is extracted from the civil service information website as well as pre-existing corpora. The evaluation of our strategy has been made on a test corpus containing 216 utterances recorded by 10 different speakers.