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- W2063601656 abstract "To overcome the computational complexity of the asynchronous hidden Markov model (AHMM), we present a novel multidimensional dynamic time warping (DTW) algorithm for hybrid fusion of asynchronous data. We show that our newly introduced multidimensional DTW concept requires significantly less decoding time while providing the same data fusion flexibility as the AHMM. Thus, it can be applied in a wide range of real-time multimodal classification tasks. Optimally exploiting mutual information during decoding even if the input streams are not synchronous, our algorithm outperforms late and early fusion techniques in a challenging bimodal speech and gesture fusion experiment." @default.
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- W2063601656 date "2009-12-01" @default.
- W2063601656 modified "2023-09-26" @default.
- W2063601656 title "A multidimensional dynamic time warping algorithm for efficient multimodal fusion of asynchronous data streams" @default.
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- W2063601656 doi "https://doi.org/10.1016/j.neucom.2009.08.005" @default.
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