Ranking Approach to Compact Text Representation for Personal Digital Assistants.

Personal digital assistants must display the output from the speech recognizer in a compact and readable representation. The process of transforming sequences from spoken words to written text is called inverse text normalization (ITN). In this paper, we present a ranking based approach to ITN that incorporates predicative information from various neural-net LSTM and n-gram models to select the best written text to display. Our approach ranks the written text candidates, generated by applying weighted FSTs to the spoken words, using a gradient boosted decision tree ensemble (GBDT). The ranker achieves a 18.48% relative reduction in word error rate over an unweighted FST system. Further, our two-stage approach allows us to decouple speech recognition from ITN and gives us greater flexibility in system configuration, since the written-form can vary by domain.