1、1會計學LSTMCRF和和CTC算法詳解純英算法詳解純英MemoryMemorynIt overcomes the gradient vanish problem, but still cant solve the problem of gradient explosionStill it can be further, just try to combine BRNNs with LSTM, and that becomes bidirectional LSTM.X: Y:start:Johnsawthetree.endThe task: given X, find YWThe CTC ne

2、twork predicts only the sequence of phonemes (typically as a series of spikes, separated by blanks). The choice of labelling can be read directly from the CTC outputs (follow the spikes).And the predictions of the framewise network must be post-processed before use.Each node either ends or extends t

3、he prefix at its parent node. The number above an extending node is the total probability beginning with that prefix. At every iteration the extensions of the most probable remaining prefix are explored.This strategy includes two aspects, one is that the same path does not repeat the calculation, an

4、d the other is that the state is no longer searched.1Speech recognition with deep recurrent neural networks. Author: Alex Graves, Abdel-Rahman Mohamed and Geoffrey Hinton2 Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks. Author: Alex Graves, Santiago Fernandez3 Neural Architectures for Named Entity Recognition. Author: Guillaume Lample, Miguel Ballest