Update README.md

README.md

@@ -14,7 +14,7 @@ pretty_name: SmoothConv
 
 # SmoothConv
 
-**SmoothConv** is a high-precision Chinese multi-channel conversational speech dataset with **expert human annotations**, released as part of the [SmoothConv & DuplexConv](https://github.com/qualialabsAI/SmoothConv-DuplexConv) project by [ASLP@NPU](https://www.npu-aslp.org) and QualiaLabs.
+**SmoothConv** is a high-quality Chinese multi-channel conversational speech dataset with **expert human annotations**, developed by [ASLP@NPU](https://www.npu-aslp.org) and QualiaLabs as part of the SmoothConv–DuplexConv corpus family.
 
 <p align="center">
   <a href="https://qualialabsai.github.io/SmoothConv-DuplexConv"><img src="https://img.shields.io/badge/Demo-Page-2563eb" alt="Demo Page"></a>
@@ -22,13 +22,13 @@ pretty_name: SmoothConv
   <a href="https://github.com/qualialabsAI/SmoothConv-DuplexConv"><img src="https://img.shields.io/badge/GitHub-Repo-green" alt="GitHub"></a>
 </p>
 
-**Companion dataset:** [**DuplexConv**](https://huggingface.co/datasets/qualialabsAI/DuplexConv) on HuggingFace (2,000 hours, LLM-assisted annotation). SmoothConv (100 hours) and DuplexConv are drawn from the same or closely related sources. Use SmoothConv for fine-grained benchmarking and DuplexConv for large-scale training.
+**Companion dataset:** [**DuplexConv**](https://huggingface.co/datasets/qualialabsAI/DuplexConv) on HuggingFace (2,000 hours, LLM-assisted annotation). SmoothConv and DuplexConv are constructed from the same underlying conversational sources. SmoothConv provides high-fidelity human annotations for benchmarking and supervised training; DuplexConv offers large-scale annotations for Speech LLM pre-training and data-driven modeling.
 
-## Dataset Summary
+## Dataset Overview
 
-SmoothConv provides **100 hours** of real-world, unscripted **multi-speaker Chinese dialogue** recorded in **multi-channel** settings across **Tutoring** and **Social Chat** scenarios. Conversations capture natural full-duplex interaction phenomena, including overlapping speech, backchannels, interruptions, and pauses.
+SmoothConv contains **100 hours** of naturally occurring **multi-party Chinese conversations** recorded in **multi-channel** environments across **Tutoring** and **Social Chat** scenarios. Unlike corpora dominated by read speech or scripted interactions, it captures realistic conversational dynamics, including overlapping speech, backchannels, interruptions, pauses, and turn transitions.
 
-Human annotators provide fine-grained labels suitable for benchmarking and supervised training on turn-taking, overlap detection, and spoken dialogue understanding.
+The dataset is **manually annotated by trained experts** and provides fine-grained conversational labels, making it suitable for turn-taking modeling, overlap and interruption detection, full-duplex spoken dialogue systems, conversational speech understanding, and Speech LLM research.
 
 | Metric | Value |
 | :--- | :---: |
@@ -50,12 +50,11 @@ Turn-taking labels include **complete**, **incomplete**, **backchannel**, and **
 
 ## Supported Tasks
 
-- Automatic speech recognition (ASR) on spontaneous multi-party speech
-- Voice activity detection (VAD) and turn-taking modeling
-- Overlap / floor-holding detection
-- Turn-state prediction
-- Paralinguistic event detection (laughter, coughs, breaths, background noise, silence, etc.)
-- Speaker attribute modeling (gender, age, emotion)
+- Turn-taking modeling
+- Overlap and interruption detection
+- Full-duplex spoken dialogue systems
+- Conversational speech understanding
+- Speech Language Models (Speech LLMs)
 
 ## Annotation Format
 
@@ -145,13 +144,14 @@ for seg in anno["instances"]:
 ```bibtex
 @article{wang2026duoconv,
   title   = {DuoConv: Large-Scale Chinese Full-Duplex Speech Datasets for Conversational AI},
-  author  = {Chengyou Wang and Chunjiang He and Jingbin Hu and Shuiyuan Wang and Bo Wu and Yuyu Ji and Jimeng Zheng and Ruofei Chen and Lei Xie},
+  author  = {Chengyou Wang and Chunjiang He and Bo Wu and Yuyu Ji and Jimeng Zheng and Ruofei Chen and Lei Xie},
   journal = {arXiv preprint arXiv:0000.00000},
   year    = {2026},
   note    = {Placeholder; paper forthcoming}
 }
 ```
 
+
 ## Contact
 
 [jimz@qualialabs.ai](mailto:jimz@qualialabs.ai)