Improve dataset card: Add metadata, benchmarks, and roadmap (#1)

- Improve dataset card: Add metadata, benchmarks, and roadmap (8627daf3dc54a7b64798455818495b2e9b8e65a8)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,8 +1,19 @@
 ---
 license: mit
+task_categories:
+- audio-classification
+language:
+- en
+- zh
+tags:
+- keyword-spotting
+- tts-synthetic
+- multilingual
+- speech-commands
+- tinyml
+- edge-ai
 ---
 
-
 # SynTTS-Commands: A Multilingual Synthetic Speech Command Dataset
 
 <!-- Badges Row -->
@@ -20,7 +31,7 @@ license: mit
 
 ## 📖 Introduction
 
-**SynTTS-Commands** is a large-scale, multilingual synthetic speech command dataset specifically designed for low-power Keyword Spotting (KWS) and speech command recognition tasks. Generated using advanced Text-to-Speech (TTS) technologies, this dataset aims to address the scarcity of high-quality training data in the fields of TinyML and Edge AI.
+**SynTTS-Commands** is a large-scale, multilingual synthetic speech command dataset specifically designed for low-power Keyword Spotting (KWS) and speech command recognition tasks. As presented in the paper [SynTTS-Commands: A Public Dataset for On-Device KWS via TTS-Synthesized Multilingual Speech](https://huggingface.co/papers/2511.07821), this dataset is generated using advanced Text-to-Speech (TTS) technologies, aiming to address the scarcity of high-quality training data in the fields of TinyML and Edge AI.
 
 ### 🎯 Core Features
 
@@ -64,7 +75,7 @@ SynTTS-Commands-Media-Dataset/
 │   ├── val/                # Validation set
 │   └── test/               # Test set
 └── comprehensive_metadata.csv # Complete metadata file
-
+```
 
 
 🎯 Media Command Categories
@@ -85,6 +96,34 @@ Volume Control: "增大音量", "减小音量", "静音", "音量调到50%", "
 Communication: "接听电话", "挂断电话", "拒接来电"
 Wake Words: "小爱同学", "Hello 小智", "小艺小艺", "嗨 三星小贝", "小度小度", "天猫精灵"
 
+## 📈 Benchmark Results and Analysis
+
+We present a comprehensive benchmark of **six representative acoustic models** on the SynTTS-Commands-Media Dataset across both English (EN) and Chinese (ZH) subsets. All models are evaluated in terms of **classification accuracy**, **cross-entropy loss**, and **parameter count**, providing insights into the trade-offs between performance and model complexity in multilingual voice command recognition.
+
+### Performance Summary
+
+| Model | EN Loss | EN Accuracy | EN Params | ZH Loss | ZH Accuracy | ZH Params |
+| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
+| **MicroCNN** | 0.2304 | 93.22% | 4,189 | 0.5579 | 80.14% | 4,255 |
+| **DS-CNN** | 0.0166 | 99.46% | 30,103 | 0.0677 | 97.18% | 30,361 |
+| **TC-ResNet** | 0.0347 | 98.87% | 68,431 | 0.0884 | 96.56% | 68,561 |
+| **CRNN** | **0.0163** | **99.50%** | 1.08M | 0.0636 | **97.42%** | 1.08M |
+| **MobileNet-V1** | 0.0167 | **99.50%** | 2.65M | **0.0552** | 97.92% | 2.65M |
+| **EfficientNet** | 0.0182 | 99.41% | 4.72M | 0.0701 | 97.93% | 4.72M |
+
+## 🗺️ Roadmap & Future Expansion
+
+We are expanding SynTTS-Commands beyond multimedia to support broader Edge AI applications. 
+
+👉 **[Click here to view our detailed Future Work Plan & Command List](https://github.com/lugan113/SynTTS-Commands-Official/blob/main/Future_Work_Plan.md)**
+
+Our upcoming domains include:
+*   🏠 **Smart Home:** Far-field commands for lighting and appliances.
+*   🚗 **In-Vehicle:** Robust commands optimized for high-noise driving environments.
+*   🚑 **Urgent Assistance:** Safety-critical keywords (e.g., "Call 911", "Help me") focusing on high recall.
+
+We invite the community to review our [Command Roadmap](https://github.com/lugan113/SynTTS-Commands-Official/blob/main/Future_Work_Plan.md) and suggest additional keywords!
+
 📜 Citation
 
 If you use this dataset in your research, please cite our paper: