FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2999130843> ?p ?o ?g. }

• W2999130843 abstract "Sensory processing is increasingly conceived in a predictive framework in which neurons would constantly process the error signal resulting from the comparison of expected and observed stimuli. Surprisingly, few data exist on the accuracy of predictions that can be computed in real sensory scenes. Here, we focus on the sensory processing of auditory and audiovisual speech. We propose a set of computational models based on artificial neural networks (mixing deep feedforward and convolutional networks), which are trained to predict future audio observations from present and past audio or audiovisual observations (i.e., including lip movements). Those predictions exploit purely local phonetic regularities with no explicit call to higher linguistic levels. Experiments are conducted on the multispeaker LibriSpeech audio speech database (around 100 hours) and on the NTCD-TIMIT audiovisual speech database (around 7 hours). They appear to be efficient in a short temporal range (25–50 ms), predicting 50% to 75% of the variance of the incoming stimulus, which could result in potentially saving up to three-quarters of the processing power. Then they quickly decrease and almost vanish after 250 ms. Adding information on the lips slightly improves predictions, with a 5% to 10% increase in explained variance. Interestingly the visual gain vanishes more slowly, and the gain is maximum for a delay of 75 ms between image and predicted sound." @default.
• W2999130843 created "2020-01-23" @default.
• W2999130843 creator A5001142101 @default.
• W2999130843 creator A5020392160 @default.
• W2999130843 creator A5059797341 @default.
• W2999130843 creator A5060372031 @default.
• W2999130843 date "2020-03-01" @default.
• W2999130843 modified "2023-09-30" @default.
• W2999130843 title "Evaluating the Potential Gain of Auditory and Audiovisual Speech-Predictive Coding Using Deep Learning" @default.
• W2999130843 cites W137138337 @default.
• W2999130843 cites W1494198834 @default.
• W2999130843 cites W1498437823 @default.
• W2999130843 cites W1503933356 @default.
• W2999130843 cites W1634005169 @default.
• W2999130843 cites W1635512741 @default.
• W2999130843 cites W1941420329 @default.
• W2999130843 cites W1955313418 @default.
• W2999130843 cites W1965248225 @default.
• W2999130843 cites W1983035758 @default.
• W2999130843 cites W1983364832 @default.
• W2999130843 cites W1990005915 @default.
• W2999130843 cites W1995562189 @default.
• W2999130843 cites W1998651249 @default.
• W2999130843 cites W2011183451 @default.
• W2999130843 cites W2016053056 @default.
• W2999130843 cites W2021569680 @default.
• W2999130843 cites W2022554507 @default.
• W2999130843 cites W2025210087 @default.
• W2999130843 cites W2038010270 @default.
• W2999130843 cites W2038548569 @default.
• W2999130843 cites W2040940389 @default.
• W2999130843 cites W2043090466 @default.
• W2999130843 cites W2051850587 @default.
• W2999130843 cites W2061960534 @default.
• W2999130843 cites W2065354509 @default.
• W2999130843 cites W2079207700 @default.
• W2999130843 cites W2081144555 @default.
• W2999130843 cites W2087148916 @default.
• W2999130843 cites W2096310022 @default.
• W2999130843 cites W2096391593 @default.
• W2999130843 cites W2097117768 @default.
• W2999130843 cites W2099940393 @default.
• W2999130843 cites W2122741244 @default.
• W2999130843 cites W2125244461 @default.
• W2999130843 cites W2125838338 @default.
• W2999130843 cites W2127958135 @default.
• W2999130843 cites W2131306847 @default.
• W2999130843 cites W2131352719 @default.
• W2999130843 cites W2137411342 @default.
• W2999130843 cites W2142352693 @default.
• W2999130843 cites W2147008239 @default.
• W2999130843 cites W2148764920 @default.
• W2999130843 cites W2149288684 @default.
• W2999130843 cites W2149740421 @default.
• W2999130843 cites W2151120830 @default.
• W2999130843 cites W2153769858 @default.
• W2999130843 cites W2154929556 @default.
• W2999130843 cites W2158358098 @default.
• W2999130843 cites W2158387459 @default.
• W2999130843 cites W2161742217 @default.
• W2999130843 cites W2171046197 @default.
• W2999130843 cites W2175538802 @default.
• W2999130843 cites W2195506630 @default.
• W2999130843 cites W2218278882 @default.
• W2999130843 cites W2267805933 @default.
• W2999130843 cites W2554625447 @default.
• W2999130843 cites W2560201830 @default.
• W2999130843 cites W2603597171 @default.
• W2999130843 cites W2747876639 @default.
• W2999130843 cites W2768153200 @default.
• W2999130843 cites W2800311957 @default.
• W2999130843 cites W2898208057 @default.
• W2999130843 cites W2919115771 @default.
• W2999130843 cites W2962866211 @default.
• W2999130843 cites W3102543255 @default.
• W2999130843 cites W3140196993 @default.
• W2999130843 cites W4237692611 @default.
• W2999130843 cites W4253654031 @default.
• W2999130843 cites W86780258 @default.
• W2999130843 cites W932459478 @default.
• W2999130843 cites W2131583525 @default.
• W2999130843 doi "https://doi.org/10.1162/neco_a_01264" @default.
• W2999130843 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31951798" @default.
• W2999130843 hasPublicationYear "2020" @default.
• W2999130843 type Work @default.
• W2999130843 sameAs 2999130843 @default.
• W2999130843 citedByCount "2" @default.
• W2999130843 countsByYear W29991308432021 @default.
• W2999130843 crossrefType "journal-article" @default.
• W2999130843 hasAuthorship W2999130843A5001142101 @default.
• W2999130843 hasAuthorship W2999130843A5020392160 @default.
• W2999130843 hasAuthorship W2999130843A5059797341 @default.
• W2999130843 hasAuthorship W2999130843A5060372031 @default.
• W2999130843 hasBestOaLocation W29991308432 @default.
• W2999130843 hasConcept C127413603 @default.
• W2999130843 hasConcept C133731056 @default.
• W2999130843 hasConcept C154945302 @default.
• W2999130843 hasConcept C15744967 @default.
• W2999130843 hasConcept C180747234 @default.
• W2999130843 hasConcept C23224414 @default.

• next