Reranked aligners for interactive transcript correction

Clarification dialogs can help address ASR errors in speech-to-speech translation systems and other interactive applications. We propose to use variants of Levenshtein alignment for merging an er-rorful utterance with a targeted rephrase of an error segment. ASR errors that might harm the alignment are addressed through phonetic matching, and a word embedding distance is used to account for the use of synonyms outside targeted segments. These features lead to a relative improvement of 30% of word error rate on sentences with ASR errors compared to not performing the clarification. Twice as many utterances are completely corrected compared to using basic word alignment. Furthermore, we generate a set of potential merges and train a neural network on crowd-sourced rephrases in order to select the best merger, leading to 24% more instances completely corrected. The system is deployed in the framework of the BOLT project.