five

ECOLANG Corpus

收藏
DataCite Commons2025-01-02 更新2025-04-17 收录
下载链接:
https://rdr.ucl.ac.uk/articles/dataset/ECOLANG_Corpus/28087613
下载链接
链接失效反馈
官方服务:
资源简介:
The ECOLANG Multimodal Corpus of adult-child and adult-adult conversation provides audiovisual recordings and annotation of multimodal communicative behaviours by English-speaking adults and children engaged in semi-naturalistic conversation.<b>Corpus</b>The corpus provides audiovisual recordings and annotation of multimodal behaviours (speech transcription, gesture, object manipulation, and eye gaze) by British and American English-speaking adults engaged in semi-naturalistic conversation with their child (N = 38, children 3-4 years old) or a familiar adult (N = 31). Speakers were asked to talk about objects (familiar or unfamiliar) to their interlocutors both when the objects were physically present or absent. Thus, the corpus characterises the use of multimodal signals in social interaction and their modulations depending upon the age of the interlocutor (child or adult); whether the interlocutor is learning new concepts/words (unfamiliar or familiar objects) and whether they can see and manipulate (present or absent) the objects.<b>Application</b>The corpus<i> </i>provides ecologically-valid data about the distribution and cooccurrence of the multimodal signals for cognitive scientists and neuroscientists to address questions about real-world language learning and processing; and for computer scientists to develop more human-like artificial agents.<b>Data access requires permission</b>.To obtain permission to view or download the video data (either viewing in your browser or downloading to your computer), please download the user license at https://www.ucl.ac.uk/pals/sites/pals/files/eula_ecolang.pdf, fill in the form and return it to ecolang@ucl.ac.uk. User licenses are granted in batches every few weeks.To view the eaf annotation files, you will need to download and install the software ELAN, available for free for Mac, Windows and Linux.<br>
提供机构:
University College London
创建时间:
2024-12-23
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作