Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recurrent neural networks (RNNs) are a porful 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. Hover RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks.  investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empors RNNs. When trained end-to-end with suitable regularisation,  find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.
['Alex Graves', 'Abdel-rahman Mohamed', 'Geoffrey E. Hinton']
I am writing a research paper on a idea Speech recognition with deep recurrent neural networks
[{"from": "human", "value": "I am writing a research paper on a idea Speech recognition with deep recurrent neural networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recurrent neural networks (RNNs) are a porful 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. Hover RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks.  investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empors RNNs. When trained end-to-end with suitable regularisation,  find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score."}]