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- W4298300178 abstract "When video is shot in noisy environment, the voice of a speaker seen in the video can be enhanced using the visible mouth movements, reducing background noise. While most existing methods use audio-only inputs, improved performance is obtained with our visual speech enhancement, based on an audio-visual neural network. We include in the training data videos to which we added the voice of the target speaker as background noise. Since the audio input is not sufficient to separate the voice of a speaker from his own voice, the trained model better exploits the visual input and generalizes well to different noise types. The proposed model outperforms prior audio visual methods on two public lipreading datasets. It is also the first to be demonstrated on a dataset not designed for lipreading, such as the weekly addresses of Barack Obama." @default.
- W4298300178 created "2022-10-01" @default.
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- W4298300178 date "2017-11-23" @default.
- W4298300178 modified "2023-09-30" @default.
- W4298300178 title "Visual Speech Enhancement" @default.
- W4298300178 doi "https://doi.org/10.48550/arxiv.1711.08789" @default.
- W4298300178 hasPublicationYear "2017" @default.
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