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• W1586532344 abstract "We replace the Hidden Markov Model (HMM) which is traditionally used in in continuous speech recognition with a bi-directional recurrent neural network encoder coupled to a recurrent neural network decoder that directly emits a stream of phonemes. The alignment between the input and output sequences is established using an attention mechanism: the decoder emits each symbol based on a context created with a subset of input symbols elected by the attention mechanism. We report initial results demonstrating that this new approach achieves phoneme error rates that are comparable to the state-of-the-art HMM-based decoders, on the TIMIT dataset." @default.
• W1586532344 created "2016-06-24" @default.
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• W1586532344 date "2014-12-04" @default.
• W1586532344 modified "2023-10-11" @default.
• W1586532344 title "End-to-end continuous speech recognition using attention-based recurrent nn: First results" @default.
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