In this paper, a method to get the best representation of a speech motion from several repetitions is presented. Each repetition is a representation of the same speech captured at different times by sequence of ultrasound images and is composed of a set of 2D spatio-temporal contours. These 2D contours in different repetitions are time aligned first by a shape based Dynamic Programming (DP) method. The best representation of the speech motion is then obtained by averaging the time aligned contours from different repetitions. Procrustes analysis is used to measure the contour similarity in the time alignment process and to get the averaged best representation. To get the point correspondence for Procrustes analysis, a nonrigid point correspondence recovery method based on a local stretching model and a global constraint is developed. Synthetic validations and experiments on real tongue motion are also presented in this paper.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1080/02699200500113863 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Dissecting the effectiveness of deep features as metric of perceptual image quality.
Neural Netw
January 2025
Image Processing Lab., Universitat de València, 46980 Paterna, Spain. Electronic address:
There is an open debate on the role of artificial networks to understand the visual brain. Internal representations of images in artificial networks develop human-like properties. In particular, evaluating distortions using differences between internal features is correlated to human perception of distortion.
View Article and Find Full Text PDFGesture recognition from surface electromyography signals based on the SE-DenseNet network.
Biomed Tech (Berl)
January 2025
College of Ocean, Jiangsu University of Science and Technology, Zhenjiang, China.
Objectives: In recent years, significant progress has been made in the research of gesture recognition using surface electromyography (sEMG) signals based on machine learning and deep learning techniques. The main motivation for sEMG gesture recognition research is to provide more natural, convenient, and personalized human-computer interaction, which makes research in this field have considerable application prospects in rehabilitation technology. However, the existing gesture recognition algorithms still need to be further improved in terms of global feature capture, model computational complexity, and generalizability.
View Article and Find Full Text PDFSpectral analysis is a widely used method for monitoring photosynthetic capacity. However, vegetation indices-based linear regression exhibits insufficient utilization of spectral information, while full spectra-based traditional machine learning has limited representational capacity (partial least squares regression) or uninterpretable (convolution). In this study, we proposed a deep learning model with enhanced interpretability based on attention and vegetation indices calculation for global spectral feature mining to accurately estimate photosynthetic capacity.
View Article and Find Full Text PDFMulti-disciplinary research will be the key to stop, restore, and end MS.
Mult Scler
January 2025
Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
The past 25 years have brought extraordinary advances in our understanding of MS pathogenesis and the subsequent development of effective therapies. Collaborative genetics efforts have uncovered the association of 236 common DNA variants with disease susceptibility and the first association with disease severity, paving the way to more effective therapies, particularly for progressive forms of the disease. In parallel, and in addition to established environmental disease triggers or modifiers, new collaborative work has revealed new associations with components of the gut microbiome.
View Article and Find Full Text PDFREDT: a specialized transformer model for the respiratory phase and adventitious sound detection.
Physiol Meas
January 2025
Nanchang University, 1st Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330031, CHINA.
Background And Objective: In contrast to respiratory sound classification, respiratory phase and adventitious sound event detection provides more detailed and accurate respiratory information, which is clinically important for respiratory disorders. However, current respiratory sound event detection models mainly use convolutional neural networks to generate frame-level predictions. A significant drawback of the frame-based model lies in its pursuit of optimal frame-level predictions rather than the best event-level ones.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!