Enhancing robustness of speech recognizers by bimodal features

In this paper a robust speech recognizer is presented based on features ob- tained from the speech signal and also from the image of the speaker. The features were combined by simple concatenation, resulting composed feature vectors to train the models corresponding to each class. For recognition, the classification process relies on a very effective algorithm, namely the multiclass SVM. Under additive noise conditions the bimodal system based on combined features acts better than the uni- modal system, based only on the speech features, the added information obtained from the image playing an important role in robustness improvement.