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Motivated by a project to create a system for people who are deaf orhard-of-hearing that would use automatic speech recognition (ASR) to producereal-time text captions of spoken English during in-person meetings withhearing individuals, we have augmented a transcript of the Switchboardconversational dialogue corpus with an overlay of word-importance annotations,with a numeric score for each word, to indicate its importance to the meaningof each dialogue turn. Further, we demonstrate the utility of this corpus bytraining an automatic word importance labeling model; our best performing modelhas an F-score of 0.60 in an ordinal 6-class word-importance classificationtask with an agreement (concordance correlation coefficient) of 0.839 with thehuman annotators (agreement score between annotators is 0.89). Finally, wediscuss our intended future applications of this resource, particularly for thetask of evaluating ASR performance, i.e. creating metrics that predictASR-output caption text usability for DHH users better thanWord Error Rate(WER).

A Corpus for Modeling Word Importance in Spoken Dialogue Transcripts