# Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset

arXiv: https://arxiv.org/abs/2203.16844 · HTML: https://mlsanity.com/paper/2203.16844/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning](https://mlsanity.com/paper/1609.06773/) (2016)
- [TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation](https://mlsanity.com/paper/1805.04699/) (2018)
- [WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition](https://mlsanity.com/paper/2110.03370/) (2021)
- [CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings](https://mlsanity.com/paper/2004.09249/) (2020)
- [Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks](https://mlsanity.com/paper/2012.14952/) (2020)
- [AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario](https://mlsanity.com/paper/2104.03603/) (2021)
- [CN-CELEB: a challenging Chinese speaker recognition dataset](https://mlsanity.com/paper/1911.01799/) (2019)
- [THCHS-30 : A Free Chinese Speech Corpus](https://mlsanity.com/paper/1512.01882/) (2015)
- [Dialog-context aware end-to-end speech recognition](https://mlsanity.com/paper/1808.02171/) (2018)

## Built on by (most cited first, 30 of 40)

- [Kimi-Audio Technical Report](https://mlsanity.com/paper/2504.18425/) (2025)
- [OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer](https://mlsanity.com/paper/2401.16658/) (2024)
- [Ming-Omni: A Unified Multimodal Model for Perception and Generation](https://mlsanity.com/paper/2506.09344/) (2025)
- [Towards Robust Speech Representation Learning for Thousands of Languages](https://mlsanity.com/paper/2407.00837/) (2024)
- [InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions](https://mlsanity.com/paper/2412.09596/) (2024)
- [Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data](https://mlsanity.com/paper/2309.13876/) (2023)
- [M2-omni: Advancing Omni-MLLM for Comprehensive Modality Support with Competitive Performance](https://mlsanity.com/paper/2502.18778/) (2025)
- [Ming-UniAudio: Speech LLM for Joint Understanding, Generation and Editing with Unified Representation](https://mlsanity.com/paper/2511.05516/) (2025)
- [NVSpeech: An Integrated and Scalable Pipeline for Human-Like Speech Modeling with Paralinguistic Vocalizations](https://mlsanity.com/paper/2508.04195/) (2025)
- [Easy Turn: Integrating Acoustic and Linguistic Modalities for Robust Turn-Taking in Full-Duplex Spoken Dialogue Systems](https://mlsanity.com/paper/2509.23938/) (2025)
- [On the Effects of Heterogeneous Data Sources on Speech-to-Text Foundation Models](https://mlsanity.com/paper/2406.09282/) (2024)
- [C3: A Bilingual Benchmark for Spoken Dialogue Models Exploring Challenges in Complex Conversations](https://mlsanity.com/paper/2507.22968/) (2025)
- [Efficient and Robust Speaker Diarization via Structured Pruning of Self-Supervised Models](https://mlsanity.com/paper/2506.18623/) (2025)
- [ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow Matching](https://mlsanity.com/paper/2507.09318/) (2025)
- [Emphasis Rendering for Conversational Text-to-Speech with Multi-modal Multi-scale Context Modeling](https://mlsanity.com/paper/2410.09524/) (2024)
- [SynParaSpeech: Automated Synthesis of Paralinguistic Datasets for Speech Generation and Understanding](https://mlsanity.com/paper/2509.14946/) (2025)
- [Towards Spontaneous Style Modeling with Semi-supervised Pre-training for Conversational Text-to-Speech Synthesis](https://mlsanity.com/paper/2308.16593/) (2023)
- [Conversational Speech Recognition by Learning Audio-textual Cross-modal Contextual Representation](https://mlsanity.com/paper/2310.14278/) (2023)
- [Target conversation extraction: Source separation using turn-taking dynamics](https://mlsanity.com/paper/2407.11277/) (2024)
- [Mamba-based Segmentation Model for Speaker Diarization](https://mlsanity.com/paper/2410.06459/) (2024)
- [The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages](https://mlsanity.com/paper/2502.15916/) (2025)
- [The Conversational Short-phrase Speaker Diarization (CSSD) Task: Dataset, Evaluation Metric and Baselines](https://mlsanity.com/paper/2208.08042/) (2022)
- [Qifusion-Net: Layer-adapted Stream/Non-stream Model for End-to-End Multi-Accent Speech Recognition](https://mlsanity.com/paper/2407.03026/) (2024)
- [Inclusivity of AI Speech in Healthcare: A Decade Look Back](https://mlsanity.com/paper/2505.10596/) (2025)
- [Qwen3.8-Omni: Towards Native Omni-Modal Agents](https://mlsanity.com/paper/2609.25611/) (2026)
- [Summary on the ISCSLP 2022 Chinese-English Code-Switching ASR Challenge](https://mlsanity.com/paper/2210.06091/) (2022)
- [TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge](https://mlsanity.com/paper/2210.14653/) (2022)
- [On the calibration of powerset speaker diarization models](https://mlsanity.com/paper/2409.15885/) (2024)
- [SpeechDialogueFactory: Generating High-Quality Speech Dialogue Data to Accelerate Your Speech-LLM Development](https://mlsanity.com/paper/2503.23848/) (2025)
- [Proactive Hearing Assistants that Isolate Egocentric Conversations](https://mlsanity.com/paper/2511.11473/) (2025)

## Abstract (arXiv)

This paper introduces a high-quality rich annotated Mandarin conversational (RAMC) speech dataset called MagicData-RAMC. The MagicData-RAMC corpus contains 180 hours of conversational speech data recorded from native speakers of Mandarin Chinese over mobile phones with a sampling rate of 16 kHz. The dialogs in MagicData-RAMC are classified into 15 diversified domains and tagged with topic labels, ranging from science and technology to ordinary life. Accurate transcription and precise speaker voice activity timestamps are manually labeled for each sample. Speakers' detailed information is also provided. As a Mandarin speech dataset designed for dialog scenarios with high quality and rich annotations, MagicData-RAMC enriches the data diversity in the Mandarin speech community and allows extensive research on a series of speech-related tasks, including automatic speech recognition, speaker diarization, topic detection, keyword search, text-to-speech, etc. We also conduct several relevant tasks and provide experimental results to help evaluate the dataset.