psifx -- Psychological and Social Interactions Feature Extraction Package
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Abstract (arXiv)
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.
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References · 10
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
- 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
- 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 492
- 2023WhisperX: Time-Accurate Speech Transcription of Long-Form Audiocited by 326
- 2023Powerset multi-class cross entropy loss for neural speaker diarizationcited by 111
- 2020A Taxonomy of Empathetic Response Intents in Human Social Conversationscited by 51
- 2021BERT meets LIWC: Exploring State-of-the-Art Language Models for Predicting Communication Behavior in Couples' Conflict Interactionscited by 8
- 2019Multimodal Deep Learning for Mental Disorders Prediction from Audio Speech Samplescited by 3
- 2019Variational Fusion for Multimodal Sentiment Analysiscited by 2
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