Adaptive training for robust spoken language understanding

Abstract

Spoken Language Understanding, as other areas of Language Technologies, suffers from a mismatching between the conditions of the training of the models and the real use of the systems. If the semantic models are estimated from the correct transcriptions of the training corpus, when the system interacts with real users, some recognition errors can not be recovered by the understanding system. To achieve an improvement in real environments we propose the use of the output sentences from the recognition process of the training corpus in order to adapt the models. To estimate these models, a labeled and segmented corpus is needed. We propose an algorithm for the automatic segmentation and labeling of the recognized sentences considering the correct segmented and labeled data as reference. Experiments with a spoken dialog corpus show that this approach outperforms the approach based on correct transcriptions.