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

An investigation of speaker independent phrase break models in End-to-End TTS systems

This paper presents our work on phrase break prediction in the context of end-to-end TTS systems, motivated by the following questions: (i) Is there any utility in incorporating an explicit phrasing model in an end-to-end TTS system?, and (ii) How do you evaluate the effectiveness of a phrasing model in an end-to-end TTS system? In particular, the utility and effectiveness of phrase break prediction models are evaluated in in the context of childrens story synthesis, using listener comprehension. We show by means of perceptual listening evaluations that there is a clear preference for stories synthesized after predicting the location of phrase breaks using a trained phrasing model, over stories directly synthesized without predicting the location of phrase breaks.Comment: Submitted for review to IEEE Acces