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

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arxiv 2306.15354 v3 pith:4DAMEIXA submitted 2023-06-27 cs.CL cs.SDeess.AS

Siqi Zheng , Luyao Cheng , Yafeng Chen , Hui Wang , Qian Chen This is my paper · ORCID
classification cs.CLcs.SDeess.AS
keywords speechd-speakerrepresentationcorpusdifferentdisentanglementinformationlarge-scale
verification ladder T0 review T1 audit T2 compute T3 formal
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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/

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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. StellarTTS: Sparse Temporal Embedding for Low-Latency and Robust Speech Synthesis

    cs.SD 2026-07 conditional novelty 6.0 of 10

    A mobile-oriented 83M-parameter masked transformer with sparse phone-anchored temporal embeddings achieves RTF 0.08 and lower WER than MaskGCT/F5-TTS on Seed-TTS test sets.

  2. VoxAging: Continuously Tracking Speaker Aging with a Large-Scale Longitudinal Dataset in English and Mandarin

    cs.SD 2025-05 conditional novelty 6.0 of 10

    A 293-speaker longitudinal dataset with weekly samples over up to 17 years is introduced and used to show that speaker verification error grows with age, especially for female and middle-aged speakers.

  3. The Multimodal Information Based Speech Processing (MISP) 2025 Challenge: Audio-Visual Diarization and Recognition

    cs.SD 2025-05 conditional novelty 5.0 of 10

    The top MISP 2025 systems achieve DER 8.09%, CER 9.48%, and cpCER 11.56%, far outperforming the provided audio-visual baselines.

  4. Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge

    eess.AS 2025-05 conditional novelty 4.0 of 10

    Extending S2SND with a channel-attention module for multi-channel audio achieves an 8.09% diarization error rate, first place in the MISP 2025 speaker diarization task.

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