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We present the results of using Hidden Markov Models (HMMs) for automatic segmentation and recognition of user motions. Previous work on recognition of user intent with man/machine interfaces has used task-level HMMs with a single hidden state for each sub-task. In contrast, many speech recognition systems employ HMMs at the phoneme level, and use a network(More)
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