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儿童和成人使用截然不同的技术指向和人类指向的语言。

Children and adults produce distinct technology- and human-directed speech.

机构信息

Phonetics Laboratory, Department of Linguistics, University of California, Davis, Davis, USA.

Language Learning Lab, Department of Psychology, University of California, Davis, Davis, USA.

出版信息

Sci Rep. 2024 Jul 6;14(1):15611. doi: 10.1038/s41598-024-66313-5.

Abstract

This study compares how English-speaking adults and children from the United States adapt their speech when talking to a real person and a smart speaker (Amazon Alexa) in a psycholinguistic experiment. Overall, participants produced more effortful speech when talking to a device (longer duration and higher pitch). These differences also varied by age: children produced even higher pitch in device-directed speech, suggesting a stronger expectation to be misunderstood by the system. In support of this, we see that after a staged recognition error by the device, children increased pitch even more. Furthermore, both adults and children displayed the same degree of variation in their responses for whether "Alexa seems like a real person or not", further indicating that children's conceptualization of the system's competence shaped their register adjustments, rather than an increased anthropomorphism response. This work speaks to models on the mechanisms underlying speech production, and human-computer interaction frameworks, providing support for routinized theories of spoken interaction with technology.

摘要

这项研究比较了说英语的成年人和儿童在心理语言学实验中与真人以及智能扬声器(亚马逊 Alexa)交谈时如何调整他们的说话方式。总体而言,参与者在与设备交谈时会发出更费力的声音(持续时间更长,音高更高)。这些差异也因年龄而异:儿童在设备导向的语音中发出更高的音高,这表明他们对系统误解的期望更强。支持这一点的是,我们看到在设备出现预期外的识别错误后,儿童的音高甚至更高。此外,成年人和儿童在“Alexa 是否像真人”这一问题上的回答都有相同程度的变化,这进一步表明,儿童对系统能力的概念化塑造了他们的语域调整,而不是增强的拟人化反应。这项工作涉及到言语产生的机制模型和人机交互框架,为与技术进行口语交互的常规理论提供了支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9489/11227501/e97742c24e54/41598_2024_66313_Fig1_HTML.jpg

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