Learning decision trees to determine turn-taking by spoken dialogue systems
Learning decision trees to determine turn-taking by spoken dialogue systems
复制标题
学习决策树以确定口语对话系统的轮流
DOI：
10.21437/icslp.2002-293
复制
发表时间：
2002
期刊：
影响因子：
--
通讯作者：
K. Aikawa
中科院分区：
文献类型：
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作者：
Ryo Sato;Ryuichiro Higashinaka;M. Tamoto;Mikio Nakano;K. Aikawa
本文提出了一种在口语对话系统中确定话轮转换时机的方法。该方法使用从人类用户和系统之间的对话语料库中学习的决策树，其中所需的话轮转换行为是手工注释的。它利用各种属性，如识别和理解结果和韵律信息。与大多数现有的系统不同，它使口语对话系统不仅可以根据停顿，还可以根据其他特征来决定话轮转换的时间，这样用户即使在他们的话语中间放置停顿也可以对系统说话。初步的实验结果表明，学习的决策树优于基线策略，它轮流在每个用户暂停。
This paper presents a method for deciding the timing of turn-taking in spoken dialogue systems. This method uses a decision tree learned from the corpus of dialogues between human users and systems in which desirable turn-taking behaviors are annotated by hand. It utilizes a variety of attributes, such as recognition and understanding results and prosodic information. Unlike most of the existing systems it enables spoken dialogue systems to decide the timing of turn-taking based on not only pauses but also other features, so that users can speak to the system even if they put pauses in the middle of their utterances. The result of a preliminary exper-iment shows that the learned decision tree outperforms the baseline strategy, which takes turn at every user pauses.
DOI：
10.21437/eurospeech.2001-396
发表时间：
2001-09
期刊：
--
影响因子：
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作者：
Akinobu Lee;Tatsuya Kawahara;K. Shikano
通讯作者：
Akinobu Lee;Tatsuya Kawahara;K. Shikano