A multiple classifier-based concept-spotting approach for robust spoken language understanding

In this paper, we present a concept spotting approach using manifold machine learning techniques for robust spoken language understanding. The goal of this approach is to find proper values for pre-defined slots of given meaning representation. Especially we propose a voting-based selection using multiple classifiers for robust spoken language understanding. This approach proposes no full level of language understanding but partial understanding because the method is only interested in the pre-defined meaning representation slots. In spite of this partial understanding, we can acquire necessary information to make interesting applications from the slot values because the slots are properly designed for specific domain-oriented understanding tasks. In several experimental results, the SLU (Spoken Language Understanding) performance degradation of spoken inputs compared with textual inputs are only F-measure 10.72, 11.43 and 11.51 for speech act, main goal and component slot extraction task respectively although the WER of spoken inputs is as high as 18.71%. That is, the evaluation results show that our concept spotting approach for SLU system is especially robust for spoken language input which has large recognition errors.