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- W2110791395 abstract "The authors propose a new system for speech recognition which results in cooperation between a multi-layer perceptron and a dynamic programming module. It is trained through a cost function inspired from learning vector quantization which approximates the empirical average risk of misclassification. All the modules of the system are trained simultaneously through gradient back-propagation, which ensures the optimality of the system. This system has achieved very good performance for isolated-word recognition problems and was trained also on continuous speech recognition. >" @default.
- W2110791395 created "2016-06-24" @default.
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- W2110791395 date "2003-01-02" @default.
- W2110791395 modified "2023-09-25" @default.
- W2110791395 title "An empirical risk optimizer for speech recognition" @default.
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- W2110791395 doi "https://doi.org/10.1109/ijcnn.1992.227236" @default.
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