Phonetic Distance Based Confidence Measure

Abstract

This letter presents a novel confidence measure for the purpose of improving user performance in Spoken Document Retrieval (SDR). The proposed confidence measure is based on the phonetic distance between subword models, employing an anti-model which is determined to be discriminative to a target model using offline training data. As an advancement from our previous work, the proposed method employs separate phonetic similarity knowledge for vowels and consonants, resulting in more reliable performance over diverse SDR recorded speech conditions. A transcript reliability estimator is also presented, with evaluation as an application of the proposed confidence measure. Analysis on a variety of corpora including background noise, frequency band-restrictions, and a range of real-life conditions, shows that the proposed confidence measure is more reliable in detecting corrupted speech due to acoustic conditions or an unarticulated speaking style, providing a higher correlation to word error rate (WER). The proposed confidence measure is effective in increasing transcript reliability estimation performance with a 16.21% relative improvement.