Joint N-Best Rescoring for Repeated Utterances in Spoken Dialog Systems - Bohus, Dan; Zweig, Geoffrey; Nguyen, Patrick; Li, Xiao (Spoken Language Technology Workshop, 2008. SLT 2008. IEEE, 2008)
Due to speech recognition errors, repetitions are a frequent phenomenon in spoken dialog systems. In previous work we have proposed a joint decoding model that can leverage structural relationships between repeated utterances for improving recogni-tion performance. In this paper we extend this work in two directions. First, we propose a direct, classification-based model for the same task. The new model can leverage features that were fundamentally hard to capture in the previous framework (e.g. spellings, false-starts, etc.) and leads to an additional performance improvement. Second, we show how both models can be used to perform a combined rescoring of two n-best lists that are part of a repetition pair.