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Combining Visual and Acoustic Modalities to Ease Speech Recognition by Hearing Impaired People

The aim of the research work presented is to show a system that facilitates speech training for hearing impaired people. The system engineered combines both visual and acoustic speech data acquisition and analysis modules. The Active Shape Model method is used for extracting visual speech features from the shape and movement of the lips. The acoustic features extraction involves mel-cepstral analysis. Artificial Neural Networks are utilized as the classifier, feature vectors extracted combine both modalities of the human speech. Additional experiments with the degraded acoustic and/or visual information are carried out in order to test the system robustness against various distortions affecting the signals.

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