Breaking News System of At-Bat Results From Sports Commentary via Speech Recognition

The proliferation of over-the-top (OTT) systems has led to a tremendous amount of video content over the Internet that users can watch them regardless of time or location restrictions. For sports games, highlight videos allow users to check the results of games without watching or listening in real-time. Despite these advances, the number of OTT companies broadcasting sports games in real-time is rising. This demonstrates that sports games are valuable video content to view in real-time. However, sports games generally last long, making continuous viewing difficult. To address this issue, X’s posts have been employed to support sports viewing to users. However, X’s limits on reading and posting and its introduction of paid features, an alternative to X need to be found. In this paper, we propose a novel approach as an alternative to X, using sports commentary to keep track of the state of a game in real-time. In order to classify players’ at-bat results from sports commentary, we combine pre-trained speech recognition and language models. In experiments, we used various pre-trained speech recognition models to create a dataset for fine-tuning, fine-tuned several pre-trained language models using this dataset, and compared the models to identify the best combination of pre-trained speech recognition and language models for classifying at-bat results. Moreover, by using both the speech recognition models and the fine-tuned language models, we compared in terms of real-time performance and classification accuracy across multiple games to verify their effectiveness.