An LVCSR Based Automatic Scoring Method in English Reading Tests

Abstract

This paper describes a reading quality scoring system based on large vocabulary continuous speech recognition (LVCSR). Our previous scoring system was based on forced alignment. A disadvantage of forced alignment based system is it can hardly catch huge kinds of reading miscues, while LVCSR based system avoids this disadvantage. The most challenge was that the LVCSR recognition rate was low on our corpus. To improve the recognition rate, we optimized our LVCSR engine for passage scoring tasks by presenting a novel dynamic language model (LM) constructing algorithm. The optimized LVCSR's recognition rate on test speech data was 70.2%, while the recognition rate of the original LVCSR on the same database was 37.9%. Our scoring method was to align the text reference and the confusion network generated from the LVCSR decoding result. The LVCSR based system reduced the scoring error rate of the baseline system by 14.5% relatively.