Efficient Visual Speech Recognition: A Survey


Efficient Visual Speech Recognition: A Survey – We present a novel method for extracting human-language dialogue from text-based systems. The technique, called Dialogue ResidualNet, is inspired and trained during the course of the IJWL 2017 Dialogue ResidualNet Competition. Dialogue ResidualNet is trained with the recent deep learning method, Deep ResidualNet, which learns to predict the dialogue with the input input. As the training step, Dialogue ResidualNet is trained with both human-human and dialogative features extracted from the input. The trained model is used to extract the human-annotated features. The evaluation shows that using the human-annotated features enables an interactive human conversation model.

We propose the Bayesian algorithm for data clustering. We show that the proposed algorithm is competitive with the state of the art (i.e., Bayesian Network) clustering algorithms and that it is also efficient for practical use with real data.

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Efficient Visual Speech Recognition: A Survey

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  • Frequency-based Feature Selection for Imbalanced Time-Series Data

    Fast Online Clustering of High-Dimensional Data with the Kronecker-factored K-nearest Neighbor RegressorWe propose the Bayesian algorithm for data clustering. We show that the proposed algorithm is competitive with the state of the art (i.e., Bayesian Network) clustering algorithms and that it is also efficient for practical use with real data.


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