Articulatory Data

Articulatory data, encompassing the movements of speech organs like the tongue, is crucial for understanding speech production and developing advanced speech technologies. Current research focuses on leveraging deep learning, particularly autoencoders, convolutional neural networks, and spatial transformer networks, to analyze and synthesize articulatory information from various sources, including ultrasound imaging, X-ray microbeam data, and even EEG signals. These efforts aim to improve speech recognition, particularly for disordered speech, create more robust silent speech interfaces, and enhance our understanding of the complex relationship between brain activity, articulatory movements, and acoustic speech. The resulting advancements have implications for both basic scientific understanding of speech and the development of improved assistive technologies.