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  1. avsp2017.loria.fr

    mostly based on PCA, image transforms such as DCT, wavelets, and scattering [6], or image descriptors like LBPs [7] and HOGs [8]. Although such features have recently been em-ployed in conjunction with deep learning methods for visual G. Potamianos wishes to acknowledge support for this work by the EU Horizon 2020 project BabyRobot, under grant ...
  2. isca-archive.org

    However, little or no attention has been paid to the effects of ROI physical coverage and resolution on the resulting recognition performance within the deep learning framework. In this paper, we investigate such choices for a visual-only speech recognition system based on CNNs and long short-term memory models that we present in detail.
  3. semanticscholar.org

    However, little or no attention has been paid to the effects of ROI physical coverage and resolution on the resulting recognition performance within the deep learning framework. In this paper, we investigate such choices for a visual-only speech recognition system based on CNNs and long short-term memory models that we present in detail.
  4. semanticscholar.org

    DOI: 10.21437/AVSP.2017-13 Corpus ID: 3531386; Exploring ROI size in deep learning based lipreading @inproceedings{Koumparoulis2017ExploringRS, title={Exploring ROI size in deep learning based lipreading}, author={Alexandros Koumparoulis and Gerasimos Potamianos and Youssef Mroueh and Steven J. Rennie}, booktitle={AVSP ..}, year={2017} }
  5. (DOI: 10.21437/AVSP.2017-13) Automatic speechreading systems have increasingly exploited deep learning advances, resulting in dramatic gains over traditional methods. State-of-the-art systems typically employ convolutional neural networks (CNNs), operating on a video region-of-interest (ROI) that contains the speaker's mouth. However, little or no attention has been paid to the effects of ...
  6. Mentioning: 12 - Automatic speechreading systems have increasingly exploited deep learning advances, resulting in dramatic gains over traditional methods. State-of-the-art systems typically employ convolutional neural networks (CNNs), operating on a video region-of-interest (ROI) that contains the speaker's mouth. However, little or no attention has been paid to the effects of ROI physical ...
  7. researchr.org

    Exploring ROI size in deep learning based lipreading. Alexandros Koumparoulis, Gerasimos Potamianos, Youssef Mroueh, Steven J. Rennie. Exploring ROI size in deep learning based lipreading. In Slim Ouni, Chris Davis 0001, Alexandra Jesse, Jonas Beskow, editors, Auditory-Visual Speech Processing, AVSP 2017, Stockholm, Sweden, 25-26 August 2017.
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