Comparison of language modeling approaches for Turkish Broadcast News

In this paper, we investigate the performance of several language modeling approaches on a speech recognition system for Turkish broadcast news. The agglutinative structure of Turkish introduces a high out-of-vocabulary rate and hence increases word error rate. To eliminate out-of-vocabulary problem, we utilize various sub-word models. In addition, we experiment with high vocabulary sizes. Since the models are statistical, we expect an improvement in performance as the amount of training data increases. We build word and sub-word language models using various amounts of corpora and compare their recognition performance.