In this paper, we propose an audio-visual speech recognition system for a person with an articulation disorder resulting from severe hearing loss. In the case of a person with this type of articulation disorder, the speech style is quite different from with the result that of people without hearing loss that a speaker-independent model for unimpaired persons is hardly useful for recognizing it. We investigate in this paper an audio-visual speech recognition system for a person with severe hearing loss in noisy environments, where a robust feature extraction method using a convolutive bottleneck network (CBN) is applied to audio-visual data. We confirmed the effectiveness of this approach through word-recognition experiments in noisy environments, where the CBN-based feature extraction method outperformed the conventional methods.
This paper reports the result of a classification experiment carried out using acoustic features for children with autism spectrum, where a new featureweighting method using a multiple kernel learning (MKL) algorithm is proposed for classification between children with autism spectrum and typically developing children. Our MKL-SVM simultaneously estimates both the classification boundary and weight of each acoustic feature, where 484 acoustic features are used in our experiments. The estimated weight indicates how acoustic features are useful for classification. Our results show the large weight acoustic features mainly for line spectral frequencies in the classification experiment using acoustic features for children with autism spectrum.
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