# 3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement

arXiv: https://arxiv.org/abs/2306.15354 · HTML: https://mlsanity.com/paper/2306.15354/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 27 Jun 2023.

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

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking](https://mlsanity.com/paper/2303.00332/) (2023)
- [Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data](https://mlsanity.com/paper/1709.07902/) (2017)
- [M2MeT: The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Challenge](https://mlsanity.com/paper/2110.07393/) (2021)
- [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)
- [An Enhanced Res2Net with Local and Global Feature Fusion for Speaker Verification](https://mlsanity.com/paper/2305.12838/) (2023)
- [Pushing the limits of self-supervised speaker verification using regularized distillation framework](https://mlsanity.com/paper/2211.04168/) (2022)
- [A Real-time Speaker Diarization System Based on Spatial Spectrum](https://mlsanity.com/paper/2107.09321/) (2021)
- [Deep Feature CycleGANs: Speaker Identity Preserving Non-parallel Microphone-Telephone Domain Adaptation for Speaker Verification](https://mlsanity.com/paper/2104.01433/) (2021)
- [BeamTransformer: Microphone Array-based Overlapping Speech Detection](https://mlsanity.com/paper/2109.04049/) (2021)
- [Deep Representation Decomposition for Rate-Invariant Speaker Verification](https://mlsanity.com/paper/2205.14294/) (2022)

## Built on by (most cited first, 15 of 15)

- [LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT](https://mlsanity.com/paper/2310.04673/) (2023)
- [OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation](https://mlsanity.com/paper/2410.17799/) (2024)
- [ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models](https://mlsanity.com/paper/2401.17230/) (2024)
- [Towards Interactive Intelligence for Digital Humans](https://mlsanity.com/paper/2512.13674/) (2025)
- [Audio2Tool: Speak, Call, Act -- A Dataset for Benchmarking Speech Tool Use](https://mlsanity.com/paper/2604.22821/) (2026)
- [The Multimodal Information Based Speech Processing (MISP) 2025 Challenge: Audio-Visual Diarization and Recognition](https://mlsanity.com/paper/2505.13971/) (2025)
- [FunCineForge: A Unified Dataset Toolkit and Model for Zero-Shot Movie Dubbing in Diverse Cinematic Scenes](https://mlsanity.com/paper/2601.14777/) (2026)
- [Robust Channel Learning for Large-Scale Radio Speaker Verification](https://mlsanity.com/paper/2406.10956/) (2024)
- [Recording for Eyes, Not Echoing to Ears: Contextualized Spoken-to-Written Conversion of ASR Transcripts](https://mlsanity.com/paper/2408.09688/) (2024)
- [UDDETTS: Unifying Discrete and Dimensional Emotions for Controllable Emotional Text-to-Speech](https://mlsanity.com/paper/2505.10599/) (2025)
- [VoxAging: Continuously Tracking Speaker Aging with a Large-Scale Longitudinal Dataset in English and Mandarin](https://mlsanity.com/paper/2505.21445/) (2025)
- [Interpolating Speaker Identities in Embedding Space for Data Expansion](https://mlsanity.com/paper/2508.19210/) (2025)
- [Towards Lightweight and Stable Zero-shot TTS with Self-distilled Representation Disentanglement](https://mlsanity.com/paper/2501.08566/) (2025)
- [Kiwano: A Cutting-Edge Open-Source Toolkit for Speaker Verification](https://mlsanity.com/paper/2606.22369/) (2026)
- [Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas](https://mlsanity.com/paper/2607.02504/) (2026)

## Abstract (arXiv)

Disentangling uncorrelated information in speech utterances is a crucial research topic within speech community. Different speech-related tasks focus on extracting distinct speech representations while minimizing the affects of other uncorrelated information. We present a large-scale speech corpus to facilitate the research of speech representation disentanglement. 3D-Speaker contains over 10,000 speakers, each of whom are simultaneously recorded by multiple Devices, locating at different Distances, and some speakers are speaking multiple Dialects. The controlled combinations of multi-dimensional audio data yield a matrix of a diverse blend of speech representation entanglement, thereby motivating intriguing methods to untangle them. The multi-domain nature of 3D-Speaker also makes it a suitable resource to evaluate large universal speech models and experiment methods of out-of-domain learning and self-supervised learning. https://3dspeaker.github.io/