Human speech ontology changes in virtual collaborative work

S. Nakata, Harumi Kobayashi, Masafumi Kumata, Satoshi Suzuki
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引用次数: 3

Abstract

We need to clarify features of human speech to make assistive robots more human adaptive. In this study, we focused on speech produced in human-human virtual collaborative conveyer task and transcribed all utterances spoken while completing the task. We computed morphemes (words or meaningful parts of words) by word class categories. The results were that the total number of morphemes spoken in each trial decreased over ten trials. We found that a strong negative correlation between the number of morphemes by type in each trial and the number of trials. Task general verbs remain while task specific verbs decreased. Among the task specific verbs, verbs with more wide meaning tend to remain. Results suggest that in accordance with acquiring skill for the task, the entire complexity of ontology decreases and becomes more efficient.
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虚拟协同工作中人类语音本体的变化
我们需要澄清人类语言的特征,使辅助机器人更具人类适应性。在本研究中,我们关注的是人-人虚拟协同传递任务中产生的语音,并转录完成任务时所说的所有话语。我们根据词类类别计算语素(单词或单词的有意义的部分)。结果表明,在10次试验中,每次试验的语素总数有所减少。我们发现,每次试验中按类型排列的语素数量与试验次数之间存在很强的负相关关系。任务一般动词保留,而任务特定动词减少。在任务特定动词中,具有更广泛意义的动词倾向于保留。结果表明,根据任务的获取技能,本体的整体复杂性降低,效率提高。
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