CUED Publications database

Detecting deletions in ASR output

Seigel, MS and Woodland, PC (2014) Detecting deletions in ASR output. In: UNSPECIFIED pp. 2302-2306..

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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. © 2014 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:01
Last Modified: 22 May 2018 06:59