Mutual information eigenlips for audio-visual speech recognition
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We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for efficient recovery of convolutive speech mixtures in spectro-temporal domain. Compared to the common sparse component analysis technique ...
Conventional linear subspace learning methods like principal component analysis (PCA), linear discriminant analysis (LDA) derive subspaces from the whole data set. These approaches have limitations in the sense that they are linear while the data distribut ...
Institute of Electrical and Electronics Engineers2011
We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for efficient recovery of convolutive speech mixtures in spectro-temporal domain. Compared to the common sparse component analysis technique ...
Communication between humans deeply relies on the capability of expressing and recognizing feelings. For this reason, research on human-machine interaction needs to focus on the recognition and simulation of emotional states, prerequisite of which is the c ...
Class-specific classifiers for audio, visual and audio-visual speech recognition systems are developed and compared with traditional classifiers. We use state- of-the-art feature extraction methods and show the benefits of a class-specific classifier on ea ...
Multilingual speech recognition obviously involves numerous research challenges, including common phoneme sets, adaptation on limited amount of training data, as well as mixed language recognition (common in many countries, like Switzerland). In this latte ...
Multilingual speech recognition obviously involves numerous research challenges, including common phoneme sets, adaptation on limited amount of training data, as well as mixed language recognition (common in many countries, like Switzerland). In this latte ...
We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for efficient recovery of convolutive speech mixtures in spectro-temporal domain. Compared to the common sparse component analysis technique ...
IEEE Service Center, 445 Hoes Lane, PO Box 1331, Piscataway, NJ 08855-1331 USA2011
Multimodal signal processing analyzes a physical phenomenon through several types of measures, or modalities. This leads to the extraction of higher-quality and more reliable information than that obtained from single-modality signals. The advantage is two ...
Humans perceive their surrounding environment in a multimodal manner by using multi-sensory inputs combined in a coordinated way. Various studies in psychology and cognitive science indicate the multimodal nature of human speech production and perception. ...
