Abstract :
In this study, some solutions for out of vocabulary (OOV) word problem of automatic speech recognition (ASR) systems which are developed for agglutinative languages like Turkish, are examined and an improvement to this problem is proposed. It has been shown that using sub-word language models outperforms word based models by reducing the OOV word ratio in languages with complex morphology. In this work we propose improvements on both statistical and morphological sub-word language modelling techniques by applying language dependent pre-processing on words before applying sub-word segmentation. In our tests, using the largest Turkish broadcast news corpus to date, we had better results in our proposed models comparing baseline statistical and morphological sub-word language models.
