Learning Score Structure from Spoken Language for a Tennis Game

We describe a novel approach to inferring the scoring rules of a tennis game by analysing the chair umpire’s speech. In a tennis match, the chair umpire, amongst other tasks, announces the scores. Hence his or her speech is the key resource for inferring the scoring rules of tennis, a task that can be accomplished by correlating the events on the court with these score announcements. In this work, the learning procedure consists of two steps: speech recognition followed by rule inference. For speech recognition, we use a two coupled language models one for words and one for scores. The first makes make use of the internal structure of a score, the second, the dependency of a score on the previous score. For rule inference, we utilize a multigram model to segment the recognised score streams into variable-length score sequences, each of them corresponding to a game in a tennis match. The approach is applied to four complete tennis matches, and shows both enhanced recognition performance, and a promising approach to inferring the scoring rules of the game. Speech recognition, rule inference, multigram model