The phenomenon of vowel reduction is investigated by modeling 'gesture reduction' with the use of the Distinctive Region Model (DRM). First, a definition is proposed for the term gesture, i.e. an acoustically efficient command aimed at deforming, in the time domain, the area function of the vocal tract. Second, tests are reported on the perception of vowel-to-vowel transitions obtained with reduced gestures. These tests show that a dual representation of formant transitions is required to explain the reduction phenomenon: the trajectory in the F(1)-F(2) plane and the time course of the formant changes. The results also suggest that time-domain integration of the trajectories constitutes an integral part of the auditory processing of transitions. Perceptual results are also discussed in terms of the acoustic traces of DRM gestures.
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http://dx.doi.org/10.1159/000028469 | DOI Listing |
Nat Commun
January 2025
Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China.
Visual sensors, including 3D light detection and ranging, neuromorphic dynamic vision sensor, and conventional frame cameras, are increasingly integrated into edge-side intelligent machines. However, their data are heterogeneous, causing complexity in system development. Moreover, conventional digital hardware is constrained by von Neumann bottleneck and the physical limit of transistor scaling.
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January 2025
University Institute of Computing, Chandigarh University, Punjab, India.
Automatic Sign Language Recognition Systems (ASLR) offers smooth communication between hearing-impaired and normal-hearing individuals, enhancing educational opportunities for impaired. However, it struggles with "curse of dimensionality" due to excessive features resulting in prolonged training time and exhaustive computational demand. This paper proposes technique that integrates machine learning and swarm intelligence to effectively address this issue.
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December 2024
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.
Surface electromyography (sEMG) data has been extensively utilized in deep learning algorithms for hand movement classification. This paper aims to introduce a novel method for hand gesture classification using sEMG data, addressing accuracy challenges seen in previous studies. We propose a U-Net architecture incorporating a MobileNetV2 encoder, enhanced by a novel Bidirectional Long Short-Term Memory (BiLSTM) and metaheuristic optimization for spatial feature extraction in hand gesture and motion recognition.
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December 2024
School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, 401331, China.
In the field of rehabilitation, although deep learning have been widely used in multitype gesture recognition via surface electromyography (sEMG), their higher algorithmic complexity often leads to low computationally inefficient, which compromise their practicality. To achieve more efficient multitype recognition, We propose the Residual-Inception-Efficient (RIE) model, which integrates Inception and efficient channel attention (ECA). The Inception, which is a multiscale fusion convolutional module, is adopted to enhance the ability to extract sEMG features.
View Article and Find Full Text PDFAdv Sci (Weinh)
December 2024
State Key Laboratory of Marine Environmental Science (MEL), College of Ocean and Earth Sciences, Xiamen University, Xiamen, Fujian, 361102, P. R. China.
Human biopotential signals, such as electrocardiography, are closely linked to health and chronic conditions. Electromyography, corresponds to muscle actions and is pertinent to human-machine interactions. Here, we present a type of smart and flexible watchband that includes a mini flexible electrode array based on Mo-Au filament mesh, combined with mesoscopic hybridized silk fibroin films.
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