Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) are the two most
common types of acoustic models used in statistical parametric approaches for generating …

This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …

It has been previously shown that, when both acoustic and articulatory training data are
available, it is possible to improve phonetic recognition accuracy by learning acoustic …

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 …

Studies have shown that articulatory information helps model speech variability and,
consequently, improves speech recognition performance. But learning speaker-invariant …

Estimating articulatory movements from speech acoustic features is known as acoustic-to-
articulatory inversion (AAI). Large amount of parallel data from speech and articulatory …

This paper presents a new spectral modeling method for statistical parametric speech
synthesis. In contrast to the conventional methods in which high-level spectral parameters …

This paper presents a deep neural network (DNN) to extract articulatory information from the
speech signal and explores different ways to use such information in a continuous speech …

Hybrid deep neural network–hidden Markov model (DNN-HMM) systems have become the
state-of-the-art in automatic speech recognition. In this paper we experiment with DNN-HMM …