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

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arxiv 2203.16844 v1 pith:4X56L55A submitted 2022-03-31 cs.CL eess.AS

Zehui Yang , Yifan Chen , Lei Luo , Runyan Yang , Lingxuan Ye , Gaofeng Cheng , Ji Xu , Yaohui Jin
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Qingqing Zhang Pengyuan Zhang Lei Xie Yonghong Yan
This is my paper
classification cs.CLeess.AS
keywords speechmagicdata-ramcmandarindatasetconversationalrichannotateddata
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. NVSpeech: An Integrated and Scalable Pipeline for Human-Like Speech Modeling with Paralinguistic Vocalizations

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  2. Sharp spectral estimates for free boundary problems arising in plasma physics

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    For a constrained superlinear free-boundary plasma model, the non-local first eigenvalue σ₁ is always positive on balls in every dimension N≥2, despite lacking a general Faber–Krahn property.

  3. The tttAI System for the TSA-ASR Task of the SmartGlasses Challenge 2026

    eess.AS 2026-07 conditional novelty 4.0 of 10

    A cascaded smart-glasses TSA-ASR system with a dominant-speaker overlap fallback achieved 7.10% tcpCER on two-person dialogues and 34.04% on multi-party meetings, ranking second on the meeting track.

  4. BoSS: Beyond-Semantic Speech

    cs.SD 2025-07 conditional novelty 4.0 of 10

    Current spoken-language models perform poorly on a new five-task evaluation of beyond-semantic speech signals, including dialect, emotion, age, and non-verbal cues.

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