Results 61 to 70 of about 619,739 (200)

Semantic Embedding Space for Zero-Shot Action Recognition [PDF]

, 2015
The number of categories for action recognition is growing rapidly. It is thus becoming increasingly hard to collect sufficient training data to learn conventional models for each category.
Gong, Shaogang, Hospedales, Timothy, Xu, Xun   +2 more
core   +1 more source

Hemispheric Organization of Visual Word Recognition in Turkish Monolinguals

Психолінгвістика, 2019
Recently obtained data from interdisciplinary research has expanded our knowledge on the relationship between language and the brain considerably. Numerous aspects of language have been the subject of research.
Filiz Mergen, Gulmira Kuruoglu
doaj   +1 more source

WaveNet With Cross-Attention for Audiovisual Speech Recognition

IEEE Access, 2020
In this paper, the WaveNet with cross-attention is proposed for Audio-Visual Automatic Speech Recognition (AV-ASR) to address multimodal feature fusion and frame alignment problems between two data streams.
Hui Wang, Fei Gao, Yue Zhao, Licheng Wu
doaj   +1 more source

How strongly do word reading times and lexical decision times correlate? Combining data from eye movement corpora and megastudies

, 2013
We assess the amount of shared variance between three measures of visual word recognition latencies: eye movement latencies, lexical decision times and naming times. After partialling out the effects of word frequency and word length, two well-documented
Baayen R.H., Balota D.A., Balota D.A., Binder K.S., Britt M.A., Brysbaert M., Brysbaert M., Brysbaert M., Coltheart M., Dambacher M., Drieghe D., Ferrand L., Forster K., Gibbs P., Inhoff A.W., Just M.A., Kennedy A., Keuleers E., Keuleers E., Kinoshita S., Kliegl R., Kuperman V., Kuperman V., Murray W.S., Murray W.S., New B., Radach R., Rayner K., Rayner K., Rayner K., Reichle E.D., Schilling H.E. H., Wotschak C.   +32 more
core   +1 more source

The Effect of Visual Word Segmentation Cues in Tibetan Reading

Brain Sciences
Background/Objectives: In languages with within-word segmentation cues, the removal or replacement of these cues in a text hinders reading and lexical recognition, and adversely affects saccade target selection during reading.
Danhui Wang, Dingyi Niu, Tianzhi Li, Xiaolei Gao   +3 more
doaj   +1 more source

N170 ERPs could represent a logographic processing strategy in visual word recognition

Behavioral and Brain Functions, 2007
Background Occipito-temporal N170 component represents the first step where face, object and word processing are discriminated along the ventral stream of the brain.
Bernard Christian, Petit Laurent, Simon Gregory, Rebaï Mohamed   +3 more
doaj   +1 more source

Dictionary-based lip reading classification [PDF]

, 2006
Visual lip reading recognition is an essential stage in many multimedia systems such as “Audio Visual Speech Recognition” [6], “Mobile Phone Visual System for deaf people”, “Sign Language Recognition System”, etc. The use of lip visual features to help
Ghita, Ovidiu, Sutherland, Alistair, Whelan, Paul F., Yu, Dahai   +3 more
core   +1 more source

On spatial selectivity and prediction across conditions with fMRI [PDF]

, 2012
Researchers in functional neuroimaging mostly use activation coordinates to formulate their hypotheses. Instead, we propose to use the full statistical images to define regions of interest (ROIs).
Schwartz, Yannick, Thirion, Bertrand, Varoquaux, Gaël   +2 more
core   +4 more sources

Supramarginal gyrus involvement in visual word recognition [PDF]

Cortex, 2009
In the classic neurological model of language, the human inferior parietal lobule (IPL) plays an important role in visual word recognition. The region is both functionally and structurally heterogeneous, however, suggesting that subregions of IPL may differentially contribute to reading.
Stoeckel, C, Gough, P, Watkins, K, Devlin, J   +3 more
openaire   +2 more sources

An Abundance of Riches: Cross-Task Comparisons of Semantic Richness Effects In Visual Word Recognition

Frontiers in Human Neuroscience, 2012
There is considerable evidence (e.g., Pexman, Hargreaves, Siakaluk, Bodner, & Pope, 2008) that semantically rich words, which are associated with relatively more semantic information, are recognized faster across different lexical processing tasks ...
Melvin J. Yap, Penny M. Pexman, Michele eWellsby, Ian S. Hargreaves, Mark eHuff   +4 more
doaj   +1 more source