Improving Co-Speech Gesture Detection by Leveraging Speech Information

Khái niệm cốt lõi

Combining speech and visual information significantly enhances the performance of co-speech gesture detection compared to using visual information alone.

Tóm tắt

The study addresses the limitations of current gesture detection approaches by focusing on co-speech gestures, which are semantically, pragmatically, and temporally linked to speech. The key highlights and insights are:

The study leverages a sliding window technique to handle the variability in gesture forms and durations, using Mel-Spectrograms for acoustic speech signals and spatiotemporal graphs for visual skeletal data.

To address the temporal misalignment and sampling rate differences between speech and gestures, the study employs separate backbone models for each modality and utilizes Transformer encoders in cross-modal and early fusion techniques to effectively align and integrate the speech and skeletal sequences.

The results show that combining visual and speech information significantly enhances gesture detection performance compared to using visual information alone. Expanding the speech buffer beyond visual time segments further improves the performance.

The study finds a correlation between the models' gesture prediction confidence and low-level speech frequency features potentially associated with gestures, indicating that speech information is promising for co-speech gesture detection.

The multimodal integration using cross-modal and early fusion techniques outperforms baseline methods using unimodal and late fusion approaches.

Thống kê

The gesture stroke duration has an average of 0.58 seconds and a median of 0.42 seconds.
The dataset contains 6,106 manually annotated gesture strokes.
The dataset consists of 19 face-to-face, task-oriented dialogues involving 38 participants across 16 hours of recorded video.

Trích dẫn

"Gestures are inherent to human interaction and often complement speech in face-to-face communication, forming a multimodal communication system."
"An important task in gesture analysis is detecting a gesture's beginning and end."
"Research on automatic gesture detection has primarily focused on visual and kinematic information to detect a limited set of isolated or silent gestures with low variability, neglecting the integration of speech and vision signals to detect gestures that co-occur with speech."

Thông tin chi tiết chính được chắt lọc từ

Leveraging Speech for Gesture Detection in Multimodal Communication

by Esam... lúc arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14952.pdf

Yêu cầu sâu hơn

How can the proposed multimodal framework be extended to detect and classify different types of co-speech gestures (e.g., iconic, metaphoric, deictic)?

The proposed multimodal framework can be extended to detect and classify different types of co-speech gestures by incorporating additional features and training the model on a more diverse dataset. To detect and classify different types of co-speech gestures, the framework can be enhanced in the following ways:

Feature Engineering: Integrate additional features related to gesture semantics, prosody, and context. For example, incorporating prosodic features such as pitch, intensity, and duration of speech segments can provide valuable information for detecting different types of gestures. Additionally, including contextual information from the dialogue or conversation can help in understanding the meaning and intent behind different types of gestures.

Gesture Segmentation: Develop algorithms for segmenting and identifying different types of gestures within the co-speech context. This involves identifying the beginning and end points of gestures, as well as distinguishing between different types of gestures such as iconic, metaphoric, and deictic gestures.

Fine-tuning Models: Train the multimodal framework on a more diverse dataset that includes a wide range of co-speech gestures. Fine-tuning the models on specific gesture categories can improve the model's ability to detect and classify different types of gestures accurately.

Hierarchical Classification: Implement a hierarchical classification approach where the model first identifies the presence of a gesture and then classifies it into specific categories based on its type. This hierarchical approach can help in handling the complexity of different types of gestures in a more structured manner.

By incorporating these enhancements, the multimodal framework can be extended to effectively detect and classify different types of co-speech gestures, including iconic, metaphoric, and deictic gestures, in a more nuanced and accurate manner.