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• W2292359288 abstract "We propose two techniques to enhance the performance of recurrent neural network (RNN)-based acoustic models. The first technique addresses training efficiency. Because RNNs require sequential input, it is difficult to randomly shuffle training samples to accelerate stochastic gradient descent based training. We propose a pseudo-shuffling procedure that instead augments training sample unexpectedness by skipping successive samples. The second proposed technique is a novel direct decoding framework in which the posterior probability of the RNN is inputted into a decoder without conversion into a hidden Markov model emission probability. In our large vocabulary speech recognition experiments with English lecture recordings, the first technique significantly improved RNN training efficiency, showing a 14.3% relative word error rate (WER) improvement. The second technique further achieved an additional 3.1% relative WER improvement. Our sigmoid-type RNN achieved a 10.7% better WER than same-sized deep neural networks without using long short-term memory cells." @default.
• W2292359288 created "2016-06-24" @default.
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• W2292359288 date "2015-12-01" @default.
• W2292359288 modified "2023-09-25" @default.
• W2292359288 title "Training data pseudo-shuffling and direct decoding framework for recurrent neural network based acoustic modeling" @default.
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• W2292359288 doi "https://doi.org/10.1109/asru.2015.7404768" @default.
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