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

To further improve the quality of the enhanced speech, it is appealing that more profound
articulatory and auditory knowledge should be introduced into the speech enhancement …

Though depression is a common mental health problem with significant impact on human
society, it often goes undetected. We explore a diverse set of features based only on spoken …

The rapid population aging has stimulated the development of assistive devices that provide
personalized medical support to the needies suffering from various etiologies. One …

We present MRI-TIMIT: a large-scale database of synchronized audio and real-time
magnetic resonance imaging (rtMRI) data for speech research. The database currently …

We investigate the problem of speaker independent acoustic-to-articulatory inversion (AAI)
in noisy conditions within the deep neural network (DNN) framework. In contrast with recent …

Articulatory features are inherently invariant to acoustic signal distortion and have been
successfully incorporated into automatic speech recognition (ASR) systems for normal …

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 …

There has been a long term interest in using visual information to improve automatic speech
recognition (ASR) system performance. Both audio and visual information are required in …

In this paper two popular feature extraction techniques Linear Predictive Cepstral
Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) have been …