no code implementations • 6 Sep 2023 • Zhihang Xu, Shaofei Zhang, Xi Wang, Jiajun Zhang, Wenning Wei, Lei He, Sheng Zhao
In this paper, we present MuLanTTS, the Microsoft end-to-end neural text-to-speech (TTS) system designed for the Blizzard Challenge 2023.
no code implementations • 9 Jan 2023 • Zhihang Xu, Yingzhi Xia, Qifeng Liao
Bayesian inverse problems are often computationally challenging when the forward model is governed by complex partial differential equations (PDEs).
no code implementations • 11 Jul 2022 • Junjie He, Zhihang Xu, Qifeng Liao
Currently, deep neural network based methods are actively developed for learning governing equations in unknown dynamic systems, but their efficiency can degenerate for switching systems, where structural changes exist at discrete time instants.
1 code implementation • 25 Oct 2021 • Yanqing Liu, Zhihang Xu, Gang Wang, Kuan Chen, Bohan Li, Xu Tan, Jinzhu Li, Lei He, Sheng Zhao
The goal of this challenge is to synthesize natural and high-quality speech from text, and we approach this goal in two perspectives: The first is to directly model and generate waveform in 48 kHz sampling rate, which brings higher perception quality than previous systems with 16 kHz or 24 kHz sampling rate; The second is to model the variation information in speech through a systematic design, which improves the prosody and naturalness.