3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and
  Multi-Dialect Corpus for Speech Representation Disentanglement
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/
649b9c6435c41eeb005e7e4f
Siqi Zheng
claimed_verified
2023-06-29T07:30:59.529Z
649cda50f97bd6fd7114333f
/avatars/ceadd0bb06a116eba5b45081f92d3243.svg
Siqi Zheng
user
ckzheng
649b9c6435c41eeb005e7e50
Luyao Cheng
649b9c6435c41eeb005e7e51
Yafeng Chen
649b9c6435c41eeb005e7e52
Hui Wang
649b9c6435c41eeb005e7e53
Qian Chen
3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and
  Multi-Dialect Corpus for Speech Representation Disentanglement
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/
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.
speech representation disentanglement 3D-Speaker devices distances dialects universal speech models out-of-domain learning self-supervised learning