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- W1603614877 abstract "We propose the prediction-adaptation-correction RNN (PAC-RNN), in which a correction DNN estimates the state posterior probability based on both the current frame and the prediction made on the past frames by a prediction DNN. The result from the main DNN is fed back to the prediction DNN to make better predictions for the future frames. In the PAC-RNN, we can consider that, given the new, current frame information, the main DNN makes a correction on the prediction made by the prediction DNN. Alternatively, it can be viewed as adapting the main DNN's behavior based on the prediction DNN's prediction. Experiments on the TIMIT phone recognition task indicate that the PAC-RNN outperforms DNN, RNN, and LSTM with 2.4%, 2.1%, and 1.9% absolute phone accuracy improvement, respectively. We found that incorporating the prediction objective and including the recurrent loop are both important to boost the performance of the PAC-RNN." @default.
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- W1603614877 date "2015-04-01" @default.
- W1603614877 modified "2023-09-22" @default.
- W1603614877 title "Speech recognition with prediction-adaptation-correction recurrent neural networks" @default.
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- W1603614877 doi "https://doi.org/10.1109/icassp.2015.7178923" @default.
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