---
task_categories:
- automatic-speech-recognition
language:
- en
- zh
tags:
- speaker-diarization
- meeting-transcription
- bilingual
license: apache-2.0
---

# Dataset Card for Multi-Talker-SD


### Dataset Description

**Multi-Talker-SD** is a large-scale bilingual (English–Mandarin) multi-speaker meeting dataset designed to support research on **speaker diarization** and **meeting transcription**.  

- **Size:** 1,000 simulated meetings  
- **Participants per meeting:** 10–30 speakers  
- **Average duration:** ~20 minutes per meeting, up to one hour  
- **Languages:** English, Mandarin (code-switching possible)  
- **Audio characteristics:** realistic speaker overlap, turn-taking patterns, reverberation, and noise injection  
- **Metadata:** speaker gender, language, session type, utterance timing  

The audio is synthesized using utterances from **AIShell-1** (Mandarin) and **LibriSpeech** (English), with added noise and reverberation to approximate real meeting conditions.

- **Curated by:** AISG Speech Lab  
- **License:** Apache-2.0  

### Dataset Sources

- **Repository:** [GitHub - Multi-Talker-SD](https://github.com/wyhzhen6/MULTI-TALKER-SD)  
- **Dataset on HF Hub:** [Multi-Talker-SD](https://huggingface.co/datasets/yihao005/Multi-Talker-SD)  

### Direct Use

- Research on **speaker diarization** under multilingual and overlapped speech conditions  
- **Meeting transcription** in bilingual settings  
- Controlled experiments on the effects of speaker metadata (gender, language, etc.)  
- Training and evaluation of **overlap-aware diarization models**  

## Dataset Structure

- **Audio files (.wav):** multi-speaker simulated meetings  
- **RTTM files:** diarization annotations with speaker labels and timestamps  
- **Metadata files:** speaker profiles including gender, language, and session type  

Each example contains:
- Meeting ID  
- List of speakers (with attributes)  
- Audio waveform  
- RTTM segmentation  

## Dataset Creation

### Source Data

- **English speech:** LibriSpeech  
- **Mandarin speech:** AIShell-1  
- **Noise sources:** point-source and diffuse-field noise corpora  
- **Processing:** audio mixing, reverberation simulation, overlap control  

### Personal and Sensitive Information

No personally identifiable or sensitive data is included. All speech is sourced from **public corpora**.