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Träfflista för sökning "WFRF:(Ananthakrishnan G) srt2:(2008-2009)"

Search: WFRF:(Ananthakrishnan G) > (2008-2009)

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1.
  • Katsamanis, N., et al. (author)
  • Audiovisual speech inversion by switching dynamical modeling Governed by a Hidden Markov Process
  • 2008
  • In: Proceedings of EUSIPCO.
  • Conference paper (peer-reviewed)abstract
    • We propose a unified framework to recover articulation from audiovisual speech. The nonlinear audiovisual-to-articulatory mapping is modeled by means of a switching linear dynamical system. Switching is governed by a state sequence determined via a Hidden Markov Model alignment process. Mel Frequency Cepstral Coefficients are extracted from audio while visual analysis is performed using Active Appearance Models. The articulatory state is represented by the coordinates of points on important articulators, e.g., tongue and lips. To evaluate our inversion approach, instead of just using the conventional correlation coefficients and root mean squared errors, we introduce a novel evaluation scheme that is more specific to the inversion problem. Prediction errors in the positions of the articulators are weighted differently depending on their relevant importance in the production of the corresponding sound. The applied weights are determined by an articulatory classification analysis using Support Vector Machines with a radial basis function kernel. Experiments are conducted in the audiovisual-articulatory MOCHA database.
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2.
  • Sen, Amrik, et al. (author)
  • Dynamic space warping of strokes for recognition of online handwritten characters
  • 2009
  • In: International journal of pattern recognition and artificial intelligence. - 0218-0014. ; 23:5, s. 925-943
  • Journal article (peer-reviewed)abstract
    • This paper suggests a scheme for classifying online handwritten characters, based on dynamic space warping of strokes within the characters. A method for segmenting components into strokes using velocity profiles is proposed. Each stroke is a simple arbitrary shape and is encoded using three attributes. Correspondence between various strokes is established using Dynamic Space Warping. A distance measure which reliably differentiates between two corresponding simple shapes (strokes) has been formulated thus obtaining a perceptual distance measure between any two characters. Tests indicate an accuracy of over 85% on two different datasets of characters.
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