language: zh
license: cc-by-nc-4.0
tags:
- speech
- conversational-speech
- chinese
pretty_name: SmoothConv
SmoothConv
SmoothConv is a high-quality Chinese multi-channel conversational speech dataset with expert human annotations, developed by ASLP@NPU and QualiaLabs as part of the SmoothConv–DuplexConv corpus family.
Companion dataset: 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 Overview
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.
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 |
|---|---|
| Total Duration | 100.53 hours |
| Audio Files | 2,503 |
| Mean Duration | 144.59 sec |
| Duration Range | 60.0 – 634.7 sec |
| Language | Chinese (zh) |
| Domains | Tutoring, Social Chat |
| Annotation | Expert human annotation |
Domains & Directory Layout
After download, each conversation is stored under a top-level folder whose name indicates the scenario. Match the folder prefix to the domain:
| Scenario | Folder prefix | Example |
|---|---|---|
| Tutoring | starts with edu or Edu |
Edu_20240101_001/ |
| Social Chat | starts with none_Edu |
none_Edu_20240101_001/ |
Within each folder you will find paired multi-channel audio (.wav) and annotation (.json) files. The same naming convention applies to both SmoothConv and DuplexConv.
Dataset Statistics
Turn-taking labels include complete, incomplete, backchannel, and wait.
Supported Tasks
- Turn-taking modeling
- Overlap and interruption detection
- Full-duplex spoken dialogue systems
- Conversational speech understanding
- Speech Language Models (Speech LLMs)
Annotation Format
Each audio file is paired with a JSON annotation file. The top-level object contains an instances list; each element describes one annotated segment.
Top-level structure
| Field | Type | Description |
|---|---|---|
instances |
list |
List of annotated segments in the conversation |
instances[i] — per-segment annotation
| Field | Type | Description |
|---|---|---|
id |
str |
Unique segment identifier (UUID) |
channelIndex |
int |
Audio channel index (0-based) |
start |
float |
Segment start time in seconds |
end |
float |
Segment end time in seconds |
text |
str |
Human-annotated transcript; inline tags mark events (e.g. <pause>, <噪声>, <unclear>) |
attributes |
dict |
Speaker, turn, paralinguistic, and scene attributes (see below) |
instances[i].attributes
| Field | Type | Description |
|---|---|---|
speaker |
str |
Speaker ID (e.g. A1, B1); unknown if unidentifiable |
turn |
str |
Turn-taking state: complete, incomplete, backchannel, wait |
other_turn |
list (optional) |
Co-occurring interaction cues, e.g. pause, unknown turn |
gender |
str |
Speaker gender |
age |
str |
Speaker age group (e.g. adult, child) |
emotion |
str |
Emotion label for the segment |
speech event |
list |
Paralinguistic / non-speech events (e.g. nonespeech event, echo, shouting) |
这段数据是在什么环境 |
str |
Scene / environment description |
Example segment
{
"id": "0d0687e7-b2e5-4b91-834b-f3e8988e7a4a",
"channelIndex": 0,
"start": 0.702,
"end": 5.146,
"attributes": {
"speaker": "A1",
"turn": "complete",
"gender": "male",
"age": "adult",
"emotion": "neutral",
"speech event": ["nonespeech event"],
"这段数据是在什么环境": "unknown"
},
"text": "春风花草香迟日江山丽日出江花红胜火"
}
Usage
import json
from datasets import load_dataset
ds = load_dataset("qualialabsAI/SmoothConv")
# Load annotation for a sample
with open("path/to/annotation.json", "r", encoding="utf-8") as f:
anno = json.load(f)
for seg in anno["instances"]:
print(seg["channelIndex"], seg["start"], seg["end"], seg["text"])
Ethics Statement
- Informed consent. Conversations were recorded with the knowledge and consent of participants. Personal identifiers have been removed or anonymized prior to release.
- Privacy protection. For academic and research use only. Do not attempt to re-identify speakers or reconstruct private information.
- Intended use. Research on spoken dialogue, turn-taking, and speech understanding—not for unauthorized surveillance, impersonation, or deceptive content generation.
- Limitations & bias. Human annotations may contain errors; account for domain and demographic bias in experiments.
- Responsible use. Report suspected misuse to jimz@qualialabs.ai.
License
Citation
@article{wang2026duoconv,
title = {DuoConv: Large-Scale Chinese Full-Duplex Speech Datasets for Conversational AI},
author = {Chengyou Wang and Chunjiang He and Zhou Zhu and Lei Xie},
journal = {arXiv preprint arXiv:0000.00000},
year = {2026},
note = {Placeholder; paper forthcoming}
}