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A Survey on Human-Centric Voice-Face Multimodal Learning.

Wuyang Chen, Kele Xu, Qiya Song

IEEE Transactions on Neural Networks and Learning Systems

|June 25, 2026

Summary

This summary is machine-generated.

This survey systematically reviews voice-face multimodal learning, integrating speech and facial data for human behavior analysis. It categorizes research into five key areas, highlighting challenges and future directions for this interdisciplinary field.

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Area of Science:

Multimodal Learning
Human-Computer Interaction
Cognitive Science

Background:

Voice-face multimodal learning integrates speech, nonverbal acoustics, and facial data for understanding human behavior.
Existing research often lacks a holistic view, remaining task-specific and overlooking unique human-centric learning properties.
This field draws upon biometric and neurocognitive principles for analyzing human-centered patterns.

Purpose of the Study:

To provide a systematic overview of voice-face multimodal learning.
To categorize existing research into five key areas: foundations, task evolution, representation learning, datasets, and bias.
To unify fragmented research and identify future directions.

Main Methods:

Systematic literature review and categorization of research.
Analysis of biometric and neurocognitive foundations.
Examination of task evolution, representation learning, dataset taxonomy, and demographic bias.
Review of past approaches and recent advancements in downstream tasks.

Main Results:

Research is categorized into five core areas, providing a structured understanding of the field.
Identified hidden dependencies between tasks through evolutionary trajectory analysis.
Highlighted the importance of human-centric representation learning and dataset properties.
Analyzed demographic bias and task-specific edge cases for fairness and robustness.

Conclusions:

A holistic perspective is needed to address the interdependencies in voice-face multimodal learning.
Further research is required in human-centric representation learning and dataset design.
Addressing bias and edge cases is crucial for robust and fair human-centric AI systems.