Automatic Speech Recognition (ASR) systems continue to make

errors during search when handling various phenomena including

noise, pronunciation variation, and out of vocabulary (OOV) words.

Predicting the probability that a word is incorrect can prevent the

error from propagating and perhaps allow the system to recover.

This paper addresses the problem of detecting errors and OOVs for

read Wall Street Journal speech when the word error rate (WER) is

very low. It augments a traditional confidence estimate by introducing

two novel methods: phone-level comparison using Multi-String

Alignment (MSA) and word-level comparison using phone-to-word

transduction. We show that features from phone and word string

comparisons can be added to a standard maximum entropy framework

thereby substantially improving performance in detecting both

errors and OOVs. Additionally we show an extension to detecting

English and accented English for the Language Identification (LID)

task.

PDF file

In  In Proceedings of ICASSP

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