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- W2892006812 abstract "Language models (LM) are very powerful in lipreading systems. Language models built upon the ground truth utterances of datasets learn grammar and structure rules of words and sentences (the latter in the case of continuous speech). However, visual co-articulation effects in visual speech signals damage the performance of visual speech LM's as visually, people do not utter what the language model expects. These models are commonplace but while higher-order N-gram LM's may improve classification rates, the cost of this model is disproportionate to the common goal of developing more accurate classifiers. So we compare which unit would best optimize a lipreading (visual speech) LM to observe their limitations. We compare three units; visemes (visual speech units) cite{lan2010improving}, phonemes (audible speech units), and words." @default.
- W2892006812 created "2018-09-27" @default.
- W2892006812 creator A5031962033 @default.
- W2892006812 date "2018-09-14" @default.
- W2892006812 modified "2023-09-24" @default.
- W2892006812 title "Visual Speech Language Models" @default.
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- W2892006812 doi "https://doi.org/10.13140/rg.2.2.15396.94081" @default.
- W2892006812 hasPublicationYear "2018" @default.
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