Statistical parametric speech synthesis (SPSS) using deep neural networks (DNNs) has
shown its potential to produce naturally-sounding synthesized speech. However, there are …

Machine learning (ML) techniques, such as neural networks, have emerged as powerful
tools for the inverse design of nanophotonic structures. However, this innovative approach …

This paper serves as an initial announcement of the availability of a corpus of articulatory
data called mngu0. This corpus will ultimately consist of a collection of multiple sources of …

This paper presents an investigation into ways of integrating articulatory features into hidden
Markov model (HMM)-based parametric speech synthesis. In broad terms, this may be …

This paper describes speech processing work in which articulator movements are used in
conjunction with the acoustic speech signal and/or linguistic information. By ''articulator …

The problem of object recognition has not yet been solved in its general form. The most
successful approach to it so far relies on object models obtained by training a statistical …

In this letter, we introduce an hidden Markov model (HMM)-based inversion system to
recovery articulatory movements from speech acoustics. Trajectory HMMs are used as …

This paper proposes a technique of continuous stochastic feature mapping based on
trajectory hidden Markov models (HMMs), which have been derived from HMMs by imposing …

Many different studies have claimed that articulatory information can be used to improve the
performance of automatic speech recognition systems. Unfortunately, such articulatory …

This paper presents an investigation into predicting the movement of a speaker's mouth from
text input using hidden Markov models (HMM). A corpus of human articulatory movements …