A spoken dialogue system based on keyword spotting technology

Abstract

In this paper, a keyword spotting based dialogue system is described. It is critical to understand user's requests accurately in a dialogue system. But the performance of large vocabulary continuous speech recognition (LVCSR) system is far from perfect, especially for spontaneous speech. In this work, an improved keyword spotting scheme is adopted instead. A fuzzy search algorithm is proposed to extract keyword hypotheses from syllable confusion networks (CN). CNs are linear and naturally suitable for indexing. To accelerate search process, CNs are pruned to feasible sizes. Furthermore, we enhance the discriminability of confidence measure by applying entropy information to the posterior probability of word hypotheses. On mandarin conversational telephone speech (CTS), the proposed algorithms obtained a 4.7% relative equal error rate (EER) reduction. © Springer-Verlag Berlin Heidelberg 2007.