Improving Spoken Language Understanding with information retrieval and active learning methods

In the context of deployed spoken dialogue telecom services, we introduce a preprocessor called fiction into the spoken language understanding (SLU) component. It acts as an intermediate between the speech recognition and interpretation processes in order to increase the rate of utterances that are correctly rejected (CRR for correctly rejected rate) without decreasing the rate of appropriately interpreted utterances. This component is based on statistical approaches of natural language treatment and contextual information. We also use active learning methods to determine the best training corpus size. On a deployed test corpus, the CRR increases from 60% to 86% and active learning method´s results show that better performance can be achieved using fewer training data.