Effective data-driven feature learning for detecting name errors in automatic speech recognition

Abstract

This paper addresses the problem of detecting name errors in automatic speech recognition (ASR) output. The highly skewed label distributions (i.e. name errors are infrequent), sparse training data, and large number of potential lexical features pose significant challenges for training name error classification systems. Data-driven feature learning is needed for handling multiple languages but is sensitive to over fitting. We address the problem by designing aggregate features using a related (sentence-level name detection) task, and reduce dimensionality of the lexical features using word classes. Experiments on conversational domain data in both English and Iraqi Arabic show that best results are obtained using all feature mapping methods plus feature selection using L1 regularization.