Diffusion models have experienced a surge of interest as highly expressive yet efficiently
trainable probabilistic models. We show that these models are an excellent fit for …

Statistical parametric speech synthesis (SPSS) combines an acoustic model and a vocoder
to render speech given a text. Typically decision tree-clustered context-dependent hidden …

This paper gives a general overview of hidden Markov model (HMM)-based speech
synthesis, which has recently been demonstrated to be very effective in synthesizing …

This paper presents a new spectral modeling method for statistical parametric speech
synthesis. In the conventional methods, high-level spectral parameters, such as mel-cepstra …

We propose using the autoregressive hidden Markov model (HMM) for speech synthesis.
The autoregressive HMM uses the same model for parameter estimation and synthesis in a …

Recurrent neural networks (RNNs) have been successfully used as fundamental frequency
(F0) models for text-to-speech synthesis. However, this paper showed that a normal RNN …

This paper proposes a statistical parametric speech synthesis technique based on Gaussian
process regression (GPR). The GPR model is designed for directly predicting frame-level …

Acoustic models used for statistical parametric speech synthesis typically incorporate many
modelling assumptions. It is an open question to what extent these assumptions limit the …

Given a transcription, sampling from a good model of acoustic feature trajectories should
result in plausible realizations of an utterance. However, samples from current probabilistic …

This paper presents sampling-based speech parameter generation using moment-matching
networks for Deep Neural Network (DNN)-based speech synthesis. Although people never …