G-g-go !Juuump !实时游戏中多关键词定位器的在线性能

J. Lehman, Nikolas Wolfe, André Pereira
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引用次数: 1

摘要

我们报告了一个在线多关键字观测者在一个游戏中的结果,该游戏包含重叠的语音,任务外的谈话,以及在完整性和持续时间上不同的关键字形式。观测者在62名儿童的数据集上进行训练,并通过对该语料库进行10倍交叉验证来建立在线表现的期望。在一项研究中,我们将事后数据与识别器的在线表现进行了比较,在这项研究中,24名新儿童玩了实时系统。在线系统显示出准确性的非显著下降,这可以追溯到理解跳跃关键字的困难以及新队列中年幼儿童的优势。然而,孩子们调整了自己的行为来弥补,在线系统的整体性能和响应性导致了引人入胜和愉快的游戏玩法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
G-g-go! Juuump! Online Performance of a Multi-keyword Spotter in a Real-time Game
We report results for an online multi-keyword spotter in a game that contains overlapping speech, off-task side talk, and keyword forms that vary in completeness and duration. The spotter trained on a data set of 62 children, and expectations for online performance were established by 10-fold cross-validation on that corpus. We compare the post hoc data to the recognizer’s performance online in a study in which 24 new children played with the real-time system. The online system showed a non-significant decline in accuracy which could be traced to trouble understanding the jump keyword and the pre-dominance of younger children in the new cohort. However, children adjusted their behavior to compensate, and the overall performance and responsiveness of the online system resulted in engaging and enjoyable gameplay.
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