title: 3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and
    Multi-Dialect Corpus for Speech Representation Disentanglement
summary: 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/
paper_authors._id: 649b9c6435c41eeb005e7e4f
paper_authors.name: Siqi Zheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-29T07:30:59.529Z
paper_authors._id: 649cda50f97bd6fd7114333f
paper_authors.avatarUrl: /avatars/ceadd0bb06a116eba5b45081f92d3243.svg
paper_authors.fullname: Siqi Zheng
paper_authors.type: user
paper_authors.user: ckzheng
paper_authors._id: 649b9c6435c41eeb005e7e50
paper_authors.name: Luyao Cheng
paper_authors._id: 649b9c6435c41eeb005e7e51
paper_authors.name: Yafeng Chen
paper_authors._id: 649b9c6435c41eeb005e7e52
paper_authors.name: Hui Wang
paper_authors._id: 649b9c6435c41eeb005e7e53
paper_authors.name: Qian Chen
paper_title: 3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and
    Multi-Dialect Corpus for Speech Representation Disentanglement
paper_summary: 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/
paper_ai_summary: A large-scale speech corpus, 3D-Speaker, facilitates research on disentangling speech representations by varying speakers, recording devices, distances, and dialects, enabling the evaluation of universal speech models and out-of-domain learning.
paper_ai_keywords: speech representation disentanglement 3D-Speaker devices distances dialects universal speech models out-of-domain learning self-supervised learning