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      Generating Intelligible Audio Speech From Visual Speech

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          Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

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            Active Shape Models-Their Training and Application

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              Speech Recognition with Deep Recurrent Neural Networks

              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 far been disappointing, with better results returned by deep feedforward networks. This paper investigates \emph{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 empowers RNNs. When trained end-to-end with suitable regularisation, we 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.
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                Author and article information

                Journal
                IEEE/ACM Transactions on Audio, Speech, and Language Processing
                IEEE/ACM Trans. Audio Speech Lang. Process.
                Institute of Electrical and Electronics Engineers (IEEE)
                2329-9290
                2329-9304
                September 2017
                September 2017
                : 25
                : 9
                : 1751-1761
                10.1109/TASLP.2017.2716178
                © 2017
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