Generating Fillers Based on Dialog Act Pairs for Smooth Turn-Taking by Humanoid Robot
Generating Fillers Based on Dialog Act Pairs for Smooth Turn-Taking by Humanoid Robot
复制标题
基于对话动作对生成填充词以实现人形机器人的平滑轮流
DOI：
10.1007/978-981-13-9443-0_8
复制
发表时间：
2018
影响因子：
0.7
通讯作者：
Tatsuya Kawahara
中科院分区：
文献类型：
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作者：
Ryosuke Nakanishi;K. Inoue;Shizuka Nakamura;K. Takanashi;Tatsuya Kawahara
在仿人机器人口语对话系统中，流畅的话轮转换功能是实现与用户自然交互的重要因素之一。当用户和对话系统同时说话时，经常发生语音冲突。本研究提出了一种在系统话语开始处生成填充词的方法，以表示像人类会话一样的话轮转换或话轮保持的意图。为此，我们分析了一个对话上下文和填料之间的关系，观察到的人机交互语料库，其中用户说话的人形机器人远程操作的人。首先，我们在对话语料库中标注了对话行为标签，并分析了对话行为序列对的典型类型，称为DA对。研究发现，根据DA对的不同，典型的填充形式和它们的出现模式是不同的。然后，我们建立了一个机器学习模型来预测出现的填充和它的适当形式从语言和韵律特征提取的前和后的话语。实验结果表明，有效的特征集也依赖于DA对的类型。
In spoken dialog systems for humanoid robots, smooth turn-taking function is one of the most important factors to realize natural interaction with users. Speech collisions often occur when a user and the dialog system speak simultaneously. This study presents a method to generate fillers at the beginning of the system utterances to indicate an intention of turn-taking or turn-holding just like human conversations. To this end, we analyzed the relationship between a dialog context and fillers observed in a human-robot interaction corpus, where a user talks with a humanoid robot remotely operated by a human. At first, we annotated dialog act tags in the dialog corpus and analyzed the typical type of a sequential pair of dialog acts, called a DA pair. It is found that the typical filler forms and their occurrence patterns are different according to the DA pairs. Then, we build a machine learning model to predict occurrence of fillers and its appropriate form from linguistic and prosodic features extracted from the preceding and the following utterances. The experimental results show that the effective feature set also depends on the type of DA pair.