8 accepted papers
Recently normalizing flows have been gaining traction in text-to-speech (TTS) and voice conversion (VC) due to their state-of-the-art (SOTA) performance. Normalizing flows are unsupervised generative models. In this paper, we introduce supervision to the training process of normalizing flows, withou…
Non-parallel voice conversion (VC) is typically achieved using lossy representations of the source speech. However, ensuring only speaker identity information is dropped whilst all other information from the source speech is retained is a large challenge. This is particularly challenging in the scen…
Prosody is an integral part of communication, but remains an open problem in state-of-the-art speech synthesis. There are two major issues faced when modelling prosody: (1) prosody varies at a slower rate compared with other content in the acoustic signal (e.g. segmental information and background n…
While recent neural text-to-speech (TTS) systems perform remarkably well, they typically require a substantial amount of recordings from the target speaker reading in the desired speaking style. In this work, we present a novel 3-step methodology to circumvent the costly operation of recording large…
Recent speech synthesis systems based on sampling from autoregressive neural network models can generate speech almost indistinguishable from human recordings. However, these models require large amounts of data. This paper shows that the lack of data from one speaker can be compensated with data fr…
Vocoding of speech is a standard part of statistical parametric speech synthesis systems. It imposes an upper bound of the naturalness that can possibly be achieved. Hybrid systems using parametric models to guide the selection of natural speech units can combine the benefits of robust statistical m…
Deep neural networks (DNNs) have recently been the focus of much text-to-speech research as a replacement for decision trees and hidden Markov models (HMMs) in statistical parametric synthesis systems. Performance improvements have been reported; however, the configuration of systems evaluated makes…
Even the best statistical parametric speech synthesis systems do not achieve the naturalness of good unit selection. We investigated possible causes of this. By constructing speech signals that lie in between natural speech and the output from a complete HMM synthesis system, we investigated various…