An LVCSR Based Reading Miscue Detection System with Knowledge of Reference and Error Patterns Dynamically Incorporated

Abstract

This paper constructs a reading miscue detection system based on conventional large vocabulary continuous speech recognition (LVCSR) framework. In order to incorporate the knowledge of reference (what the reader ought to read) and some error patterns into the decoding process, two methods are proposed: dynamic interpolation of language model (DILM) and dynamic multiple pronunciation incorporation (DMPI). DILM dynamically interpolates the general language model based on analysis of reference and so restricts the active paths of decoding not too far away from the reference. It makes the recognition more accurate, which further improve the detection performance. DMPI dynamically adds some pronunciation variations into the search space to predict reading substitutions. It solves the confliction between the coverage of error predictions and the perplexity the search space. The experimental results show that the proposed two methods can totally decrease EER by 14% relatively totally, from 46.4% to 39.8%.