🎥 MLD-VC: Multimodal Dataset for Video Conferencing

When AVSR Meets Video Conferencing: Dataset, Degradation, and the Hidden Mechanism Behind Performance Collapse (CVPR 2026) 📄 [Paper] | 🤗 [Hugging Face Dataset]

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📌 Overview

MLD-VC is the first multimodal dataset specifically designed for Audio-Visual Speech Recognition (AVSR) in real-world video conferencing (VC) scenarios.

Unlike traditional AVSR datasets collected in controlled offline environments, MLD-VC explicitly models two critical factors in VC:

• Transmission Distortions (compression, speech enhancement, etc.)
• Human Hyper-expression (e.g., Lombard effect)

🔍 Key Features

• 🎤 31 speakers, 22.79 hours of recordings
• 🌐 4 mainstream VC platforms
• 🗣️ Bilingual: English & Chinese
• 🎧 Lombard effect simulation via noise conditions
• 🎥 Multimodal data:
  • Video
  • Audio
  • Facial landmarks
  • text

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🚨 Motivation

Existing AVSR systems show severe performance degradation in video conferencing, due to:

• Distribution shift caused by speech enhancement algorithms
• Behavioral changes such as hyper-expression

MLD-VC is designed to bridge the gap between offline datasets and real-world VC deployment.

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📂 Dataset Structure

The dataset is organized into three aligned modalities:

MLD-VC/
├── video/
├── audio/
├── landmarks/

Each modality follows the same hierarchical structure:

<modality>/
└── Online / Offline
    └── speaker_id
        └── platform
            └── sentence_id
                └── clean / 40db / 60db / 80db

📖 Example

video/
└── Online/
    └── speaker_03/
        └── Zoom/
            └── sentence_012/
                ├── clean/
                ├── 40db/
                ├── 60db/
                └── 80db/

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🧠 Data Description

1. Online vs Offline

• Offline:
  • Direct recording (no transmission)
  • Contains hyper-expression (via noise)
• Online:
  • Recorded after transmission through VC platforms
  • Includes:
    • Compression
    • Speech enhancement
    • Network effects

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2. Noise Levels (Lombard Effect)

Each sentence is recorded under 4 noise conditions:

Condition	Description
clean	No noise
40dB	Mild noise
60dB	Moderate noise
80dB	Strong noise

These simulate Lombard effect intensity, inducing hyper-expression.

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3. Platforms

The dataset includes recordings from multiple VC platforms (e.g.):

• Zoom
• Tencent Meeting
• Lark
• DingTalk

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⚠️ Important Notes

🔁 Recording Protocol Differences

• In Offline subset:
  • Speakers 2–8:
    • Recorded on a single device, repeated across 4 platforms
  • Other speakers:
    • DD platform only, but actually recorded using 4 different devices simultaneously

👉 This leads to:

• Platform variation ≠ always device variation
• Be careful in cross-platform generalization experiments

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❌ Removed Speakers

• Speaker 0 and 1 have been removed
  • Due to poor recording quality

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📏 Data Consistency

• All three modalities (video, audio, landmarks) are:
  • Strictly aligned
  • Share identical folder structure
  • Can be indexed jointly

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🔬 Recommended Use Cases

MLD-VC is suitable for:

✔ AVSR Robustness

• Evaluate performance under real VC conditions

✔ Cross-domain Generalization

• Train on Offline → Test on Online

✔ Multimodal Learning

• Audio-visual fusion
• Landmark-based modeling

✔ Distribution Shift Analysis

• Study impact of:
  • Speech enhancement
  • Lombard effect

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📊 Key Findings (from the paper)

• AVSR models suffer massive degradation in VC
• Speech enhancement is the main cause of audio distribution shift
• Lombard effect ≈ VC distortion (in feature space)
• Landmark-based features are more stable than image features
• Fine-tuning on MLD-VC reduces CER by 17.5%

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📎 Citation

If you find this dataset useful, please cite:

@inproceedings{huang2026mldvc,
  title={When AVSR Meets Video Conferencing: Dataset, Degradation, and the Hidden Mechanism Behind Performance Collapse},
  author={Huang, Yihuan and Xue, Jun and Liu, Jiajun and Li, Daixian and Zhang, Tong and Yi, Zhuolin and Ren, Yanzhen and Li, Kai},
  booktitle={CVPR},
  year={2026}
}

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🙏 Acknowledgements

This work is supported by:

• National Natural Science Foundation of China
• DiDi Chuxing Group

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📬 Contact

If you have questions, feel free to contact:

• Yihuan Huang: yihuanhuang@whu.edu.cn

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