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

TL;DR

The paper introduces 3D-Speaker, a comprehensive large-scale speech corpus with multi-device, multi-distance, and multi-dialect recordings, designed to advance research in speech representation disentanglement and evaluation of speech models.

Contribution

It provides a novel, large-scale multi-dimensional speech dataset that enables disentanglement research and evaluation of universal speech models across diverse conditions.

Findings

The corpus includes over 10,000 speakers with multi-device and multi-distance recordings.

It facilitates research on speech representation disentanglement and out-of-domain learning.

The dataset supports evaluation of self-supervised speech models.

Abstract

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/