Detecting deletions in ASR output
M.S. Seigel, Philip C. Woodland · 2014
In this work, the novel task of detecting deletions within automatic speech recognition (ASR) system output is investigated. Deletion-informed confidence estimation is proposed as an approach which simultaneously yields a confidence score in a word being correct, as well as a deletion confidence score which indicates whether a deletion is likely to occur in the output. The sequential nature of conditional random field (CRF) models is exploited as a means through which this can be achieved. It is shown that this sequence structure is crucial in yielding useful deletion detection scores, with an equivalent non-sequential model proven to be unsuitable for the task. The deletion-informed confidence estimation approach is also shown to outperform one where deletion confidence scores are estimated as a classification task separate from that of overall confidence estimation.