Although much is known about how speech is produced, and research into speech
production has resulted in measured articulatory data, feature systems of different kinds, and …

In this paper, we describe a novel spectral conversion method for voice conversion (VC). A
Gaussian mixture model (GMM) of the joint probability density of source and target features …

Deep learning is becoming an increasingly important way to produce accurate hydrological
predictions across a wide range of spatial and temporal scales. Uncertainty estimations are …

This paper describes the acquisition of a new database of dysarthric speech in terms of
aligned acoustics and articulatory data. This database currently includes data from seven …

In this paper, we describe a statistical approach to both an articulatory-to-acoustic map**
and an acoustic-to-articulatory inversion map** without using phonetic information. The …

To solve the acoustic-to-articulatory inversion problem, this paper proposes a deep
bidirectional long short term memory recurrent neural network and a deep recurrent mixture …

An artificial neural network (ANN) is a powerful mathematical framework used to either
model complex relationships between inputs and outputs or find patterns in data. It is based …

This paper proposes a trajectory model which is based on a mixture density network trained
with target features augmented with dynamic features together with an algorithm for …

Speech inversion is a well-known ill-posed problem and addition of speaker differences
typically makes it even harder. Normalizing the speaker differences is essential to effectively …

We implement two deep architectures for the acoustic-articulatory inversion map**
problem: a deep neural network and a deep trajectory mixture density network. We find that …