Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
