Confidence measures for speech recognition: A survey

By Hui Jiang. Speech Communication, 2005

434 citations recorded by the source corpus.

How researchers see this paper

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Towards Transparent Systems: Semantic Characterization of Failure Modes (2014, background): “Applications such as spam-ﬁltering [5], natural language processing [6, 7], speech [8] and even computer vision [9] have leveraged these ideas.”
Predicting speech recognition confidence using deep learning with word identity and score features (2013, methodology): “Refer to [4] for a survery of these techniques.”
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces (2018, background): “Confidence scoring is a notoriously hard problem in speech recognition [20, 59].”
A big data approach to acoustic model training corpus selection (2014, methodology): “To guarantee good quality in the transcriptions we rely on utterance level confidence metrics derived from lattice posterior word probabilities [11].”
Calibration of Confidence Measures in Speech Recognition (2011, background): “This is especially true when irrelevant or redundant words, such as ma’am in “yes ma’am” and ah in “ah yes,” are misrecognized, or filtered out (e.g., using a garbage model [4], [5].)”
Enhanced Phone Posteriors for Improving Speech Recognition Systems (2010, methodology): “Posterior probabilities have also been used as local measur s for different ASR purposes, such as (1) estimating confidenc e measures [7], [15], [16], [17], (2) beam search pruning [18] , or (3) word lattice rescoring [19].”