Joint N-Best Rescoring For Repeated Utterances In Spoken Dialog Systems

Due to speech recognition errors, repetitions are a frequent phenomenon in spoken dialog systems. In previous work [1] we have proposed a joint decoding model that can leverage structural relationships between repeated utterances for improving recognition 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.