Analysis of the recognition errors in LVCSR of Turkish

This paper presents the analysis of recognition errors in large vocabulary continuous speech recognition (LVCSR) of Turkish. This analysis aims to learn the source of the recognition errors and investigate useful features to rectify them. These features will be used in corrective language models. First, recognition experiments were performed using word and sub-word (morph) language models. Morphs outperformed words for out-of-vocabulary words and achieved 1.5% absolute significant improvements over words. Then, the errors in the recognition output of the morph model were manually labeled according to the predefined error classes. This subjective labeling revealed that errors due to incorrect syntax can be corrected. Therefore, using syntactic dependency relations as features in the corrective language models is expected to yield higher accuracies.