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Found 4 papers, 1 papers with code
MuLanTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2023
In this paper, we present MuLanTTS, the Microsoft end-to-end neural text-to-speech (TTS) system designed for the Blizzard Challenge 2023.
A domain-decomposed VAE method for Bayesian inverse problems
Bayesian inverse problems are often computationally challenging when the forward model is governed by complex partial differential equations (PDEs).
Deep neural network based adaptive learning for switched systems
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.
DelightfulTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2021
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.
