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README.md

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-license: mit
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+# 🎥 MLD-VC: Multimodal Dataset for Video Conferencing
+
+> **When AVSR Meets Video Conferencing: Dataset, Degradation, and the Hidden Mechanism Behind Performance Collapse (CVPR 2026)**
+> 📄 [[Paper\]](https://arxiv.org/abs/2603.22915) | 🤗 [[Hugging Face Dataset\]](https://huggingface.co/datasets/nccm2p2/MLD-VC)
+
+------
+
+## 📌 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
+
+------
+
+## 🚨 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**.
+
+------
+
+## 📂 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/
+```
+
+------
+
+## 🧠 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
+
+------
+
+### 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.
+
+------
+
+### 3. Platforms
+
+The dataset includes recordings from multiple VC platforms (e.g.):
+
+- Zoom
+- Tencent Meeting
+- Lark
+- DingTalk
+
+------
+
+## ⚠️ 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**
+
+------
+
+### ❌ Removed Speakers
+
+- **Speaker 0 and 1 have been removed**
+  - Due to poor recording quality
+
+------
+
+### 📏 Data Consistency
+
+- All three modalities (`video`, `audio`, `landmarks`) are:
+  - **Strictly aligned**
+  - Share identical folder structure
+  - Can be indexed jointly
+
+------
+
+## 🔬 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
+
+------
+
+## 📊 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%**
+
+------
+
+## 📎 Citation
+
+If you find this dataset useful, please cite:
+
+```bibtex
+@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}
+}
+```
+
+------
+
+## 🙏 Acknowledgements
+
+This work is supported by:
+
+- National Natural Science Foundation of China
+- DiDi Chuxing Group
+
+------
+
+## 📬 Contact
+
+If you have questions, feel free to contact:
+
+- **Yihuan Huang**: [yihuanhuang@whu.edu.cn](mailto:yihuanhuang@whu.edu.cn)
+
+------
+
+## ⭐ Star This Repo
+
+If you find MLD-VC helpful, please consider giving a ⭐!
+