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Speech recognition with deep recurrent neural networks
2013
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so
doi:10.1109/icassp.2013.6638947
dblp:conf/icassp/GravesMH13
fatcat:5f2ghhhs55f2rdi6cvjgt3a5km