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Computer Science > Computation and Language

arXiv:2407.10266 (cs)
[Submitted on 14 Jul 2024 (v1) , last revised 17 Jul 2025 (this version, v4)]

Title: psifx -- Psychological and Social Interactions Feature Extraction Package

Title: psifx -- 心理和社会交互特征提取包

Authors:Guillaume Rochette, Mathieu Rochat, Matthew J. Vowels
Abstract: psifx is a plug-and-play multi-modal feature extraction toolkit, aiming to facilitate and democratize the use of state-of-the-art machine learning techniques for human sciences research. It is motivated by a need (a) to automate and standardize data annotation processes that typically require expensive, lengthy, and inconsistent human labour; (b) to develop and distribute open-source community-driven psychology research software; and (c) to enable large-scale access and ease of use for non-expert users. The framework contains an array of tools for tasks such as speaker diarization, closed-caption transcription and translation from audio; body, hand, and facial pose estimation and gaze tracking with multi-person tracking from video; and interactive textual feature extraction supported by large language models. The package has been designed with a modular and task-oriented approach, enabling the community to add or update new tools easily. This combination creates new opportunities for in-depth study of real-time behavioral phenomena in psychological and social science research.
Abstract: psifx是一个即插即用的多模态特征提取工具包,旨在促进并使人类科学研究中最新机器学习技术的使用更加普及。 它的动机源于以下需求:(a) 自动化和标准化通常需要昂贵、耗时且不一致的人工劳动的数据标注过程;(b) 开发和分发开源社区驱动的心理学研究软件;以及(c) 为非专业用户提供大规模访问和易于使用的体验。 该框架包含一系列用于任务的工具,例如从音频中进行说话人辨识、闭路字幕的转录和翻译;从视频中进行多人跟踪的身体、手部和面部姿态估计以及注视跟踪;以及由大型语言模型支持的交互式文本特征提取。 该软件包采用模块化和任务导向的方法设计,使社区能够轻松添加或更新新工具。 这种组合为心理学和社会科学研究中对实时行为现象的深入研究创造了新的机会。
Subjects: Computation and Language (cs.CL) ; Machine Learning (cs.LG)
Cite as: arXiv:2407.10266 [cs.CL]
  (or arXiv:2407.10266v4 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2407.10266
arXiv-issued DOI via DataCite

Submission history

From: Matthew Vowels [view email]
[v1] Sun, 14 Jul 2024 16:20:42 UTC (3,664 KB)
[v2] Tue, 16 Jul 2024 09:30:03 UTC (3,664 KB)
[v3] Mon, 9 Dec 2024 09:14:47 UTC (3,706 KB)
[v4] Thu, 17 Jul 2025 18:29:50 UTC (627 KB)
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