REWIND Dataset: Privacy-preserving Speaking Status Segmentation from Multimodal Body Movement Signals in the Wild

Core Concepts

The author presents the first publicly available multimodal dataset, REWIND, for speaking status segmentation from body movement in real-life mingling scenarios with high-quality audio recordings.

Abstract

The REWIND dataset addresses challenges of detecting speaking status in crowded mingling scenarios without high-quality audio recordings. It introduces a novel approach to segmenting speaking status using video, pose tracks, and acceleration data. The dataset enables cross-modality studies and offers new insights into social interactions through body movement analysis.

Stats

"We present three baselines for no-audio speaking status segmentation: a) from video, b) from body acceleration (chest-worn accelerometer), c) from body pose tracks."
"In all cases we predict a 20Hz binary speaking status signal extracted from the audio."
"The dataset includes three modalities capturing body movements: video, pose, and wearable acceleration."
"Our contributions include introducing the REWIND dataset with high-quality raw audio, video, and acceleration; automatic pose annotations; and automatic speaking status labels."
"Results suggest the superiority of combining modalities for the task."
"Results show that combining video, poses, and acceleration outperforms individual modality-based methods."

Quotes

"We present three baselines for no-audio speaking status segmentation: a) from video, b) from body acceleration (chest-worn accelerometer), c) from body pose tracks."
"In all cases we predict a 20Hz binary speaking status signal extracted from the audio."
"The dataset includes three modalities capturing body movements: video, pose, and wearable acceleration."

Key Insights Distilled From

REWIND Dataset

by Jose Vargas ... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01229.pdf

Deeper Inquiries

How can the REWIND dataset be utilized to advance research on social interaction analysis beyond speaking status segmentation

The REWIND dataset presents a valuable opportunity to advance research on social interaction analysis beyond speaking status segmentation. With high-quality audio recordings and annotations, researchers can delve into the intricate relationship between vocal production and body movement in naturalistic social interactions. This dataset allows for the exploration of various multimodal constructs such as affect, engagement, or enjoyment that manifest in both vocal cues and body movements. By leveraging the raw data from REWIND, researchers can train models to detect and analyze these social signals at a higher temporal resolution.
Furthermore, REWIND opens avenues for studying how different labeling conditions (such as video-based labels) impact label reliability and model performance. Researchers can investigate the trade-offs involved in annotating inherently multimodal phenomena from limited modalities like video or audio. The availability of ground truth audio enables manual verification or automatic refinement of annotations, enhancing the quality of data for future studies.
In addition to speaking status segmentation, researchers can explore laughter detection, intensity estimation, back-channeling behaviors, group dynamics analysis, affect recognition, engagement levels assessment during conversations using this rich dataset. By training action detectors with diverse input modalities and labeling schemes from REWIND data points towards a more comprehensive understanding of social interactions beyond just speech-related activities.

What are the potential limitations of using noisy pose tracks for analyzing speech-related gestures in crowded settings

One potential limitation of using noisy pose tracks for analyzing speech-related gestures in crowded settings is the challenge posed by inaccuracies introduced by noise factors such as occlusion and cross-contamination. While pose tracking algorithms may provide reasonable track association performance overall, they might struggle to differentiate speech-related gestures from pose noise effectively due to their relative nature.
The noisy nature of poses obtained from systems like those used in REWIND could lead to missed subjects—especially those distant from cameras—and sporadic track misassignments when individuals walk across each other's paths or stand close together within frames. These challenges make it harder for models to separate meaningful speech-related gestures accurately amidst pose noise.
Moreover, noisy pose tracks may introduce errors that impact the overall performance of gesture recognition models trained on them. Inaccurate associations between poses across frames could hinder the ability to distinguish subtle speech-related movements effectively within crowded settings where multiple individuals interact simultaneously.

How might mixed-consent data collection designs like those in REWIND impact the analysis of group-level social signals

Mixed-consent data collection designs like those implemented in datasets such as REWIND can have significant implications for analyzing group-level social signals:

Impact on Data Completeness: Mixed-consent designs may result in incomplete datasets where only certain participants wear sensors or consent to specific modalities like video recording or accelerometer usage.

Challenges with Group-Level Analysis: Analyzing group-level social signals becomes challenging when not all members are represented with sensor data uniformly across modalities.

Biases in Data Representation: The absence of sensor data from some participants introduces biases into analyses focusing on group dynamics or collective behavior.

Opportunities for Individual-Level Analysis: Despite limitations at the group level due to mixed consent designs, individual-level analyses remain feasible using available sensor data even if not all participants contribute equally.

5 .Ethical Considerations: Ensuring privacy concerns are addressed appropriately becomes crucial when dealing with mixed-consent datasets where some individuals opt-out of certain sensing modalities while others participate fully.
These considerations highlight both challenges and opportunities associated with mixed-consent data collection approaches concerning their impact on analyzing group-level social signals during interactions captured by datasets like REWIND."