是有人在还是电视在响?使用声音检测社会存在

IF 4.2 Q2 ROBOTICS ACM Transactions on Human-Robot Interaction Pub Date : 2023-08-18 DOI:10.1145/3611658
Nicholas C Georgiou, Rebecca Ramnauth, Emmanuel Adéníran, Michael Lee, Lila Selin, B. Scassellati
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引用次数: 0

摘要

家庭中的社交机器人将需要解决音频识别问题,以便更好地与用户互动。本文关注的是a)包括至少一个用户的自然对话和b)从电子来源播放的媒体,不需要社会回应,如电视节目。这种分类可以帮助社交机器人通过声音来检测用户的社交存在。能够解决这个问题的社交机器人可以应用这些信息来帮助它们做出决策,例如确定何时以及如何适当地吸引人类用户。我们从各种声学环境中编译了一个数据集,其中包含自然或媒体音频,包括我们在自己家中录制的音频。使用该数据集,我们对一系列传统机器学习分类器进行了实验评估,并评估了分类器泛化到新录音、声学条件和环境的能力。我们得出结论,c -支持向量分类(SVC)算法优于其他分类器。最后,我们提出了一个家用机器人可以使用的分类管道,并讨论了训练分类器的时间和大小,以及隐私和道德考虑。
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Is Someone There Or Is That The TV? Detecting Social Presence Using Sound
Social robots in the home will need to solve audio identification problems to better interact with their users. This paper focuses on the classification between a) natural conversation that includes at least one co-located user and b) media that is playing from electronic sources and does not require a social response, such as television shows. This classification can help social robots detect a user’s social presence using sound. Social robots that are able to solve this problem can apply this information to assist them in making decisions, such as determining when and how to appropriately engage human users. We compiled a dataset from a variety of acoustic environments which contained either natural or media audio, including audio that we recorded in our own homes. Using this dataset, we performed an experimental evaluation on a range of traditional machine learning classifiers, and assessed the classifiers’ abilities to generalize to new recordings, acoustic conditions, and environments. We conclude that a C-Support Vector Classification (SVC) algorithm outperformed other classifiers. Finally, we present a classification pipeline that in-home robots can utilize, and discuss the timing and size of the trained classifiers, as well as privacy and ethics considerations.
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来源期刊
ACM Transactions on Human-Robot Interaction
ACM Transactions on Human-Robot Interaction Computer Science-Artificial Intelligence
CiteScore
7.70
自引率
5.90%
发文量
65
期刊介绍: ACM Transactions on Human-Robot Interaction (THRI) is a prestigious Gold Open Access journal that aspires to lead the field of human-robot interaction as a top-tier, peer-reviewed, interdisciplinary publication. The journal prioritizes articles that significantly contribute to the current state of the art, enhance overall knowledge, have a broad appeal, and are accessible to a diverse audience. Submissions are expected to meet a high scholarly standard, and authors are encouraged to ensure their research is well-presented, advancing the understanding of human-robot interaction, adding cutting-edge or general insights to the field, or challenging current perspectives in this research domain. THRI warmly invites well-crafted paper submissions from a variety of disciplines, encompassing robotics, computer science, engineering, design, and the behavioral and social sciences. The scholarly articles published in THRI may cover a range of topics such as the nature of human interactions with robots and robotic technologies, methods to enhance or enable novel forms of interaction, and the societal or organizational impacts of these interactions. The editorial team is also keen on receiving proposals for special issues that focus on specific technical challenges or that apply human-robot interaction research to further areas like social computing, consumer behavior, health, and education.
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