Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes an application of information theoretic approach for finding the most informative subset of eigen-features to be used for audio-visual speech recognition tasks. The state-of-the-art visual feature extraction methods in the area of speechreading rely on either pixel or geometric based methods or their combination. Hover, there is no common rule defining how these features have to be selected with respect to the chosen set of audio cues and how ll they represent the classes of the uttered speech. Our main objective is to exploit the complementarity of audio and visual sources and select meaningful visual descriptors by the means of mutual information.  focus on the principal components projections of the mouth region images and apply the proposed method such that only those cues having the highest mutual information with word classes are retained. The algorithm is tested by performing various speech recognition experiments on a chosen audio-visual dataset. The obtained recognition rates are compared to those acquired using a conventional principal component analysis and promising results are shown.
['Ivana Arsic', 'Jean-Philippe Thiran']
I am writing a research paper on a idea Mutual information eigenlips for audio-visual speech recognition
[{"from": "human", "value": "I am writing a research paper on a idea Mutual information eigenlips for audio-visual speech recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes an application of information theoretic approach for finding the most informative subset of eigen-features to be used for audio-visual speech recognition tasks. The state-of-the-art visual feature extraction methods in the area of speechreading rely on either pixel or geometric based methods or their combination. Hover, there is no common rule defining how these features have to be selected with respect to the chosen set of audio cues and how ll they represent the classes of the uttered speech. Our main objective is to exploit the complementarity of audio and visual sources and select meaningful visual descriptors by the means of mutual information.  focus on the principal components projections of the mouth region images and apply the proposed method such that only those cues having the highest mutual information with word classes are retained. The algorithm is tested by performing various speech recognition experiments on a chosen audio-visual dataset. The obtained recognition rates are compared to those acquired using a conventional principal component analysis and promising results are shown."}]