Concept-based phrase spotting approach for spontaneous speech understanding

In order to realize robust speech understanding, we present a phrase spotting approach. The use of concept-based phrases as the core unit for understanding is advantageous because the phrase-level constraint realizes wider coverage and stable matching and they are directly mapped to semantic cases. The phrase spotting and the sentence-level parsing are formulated as a progressive search. It realizes an optimal search to spot phrase candidates with Viterbi scoring and A* search to combine the phrase candidates into optimal sentence hypotheses. The approach achieved higher detection rates and robust interpretation of ill-formed utterances. We also examined the effect of the background language model. It is shown that lexical knowledge in the background is vital for spotting and the use of the acoustic score of the filler model is significant for parsing.