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      Automatic diacritization of Arabic text using recurrent neural networks

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          Framewise phoneme classification with bidirectional LSTM and other neural network architectures.

          In this paper, we present bidirectional Long Short Term Memory (LSTM) networks, and a modified, full gradient version of the LSTM learning algorithm. We evaluate Bidirectional LSTM (BLSTM) and several other network architectures on the benchmark task of framewise phoneme classification, using the TIMIT database. Our main findings are that bidirectional networks outperform unidirectional ones, and Long Short Term Memory (LSTM) is much faster and also more accurate than both standard Recurrent Neural Nets (RNNs) and time-windowed Multilayer Perceptrons (MLPs). Our results support the view that contextual information is crucial to speech processing, and suggest that BLSTM is an effective architecture with which to exploit it.
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            Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition

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              Supervised Sequence Labelling with Recurrent Neural Networks

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                Author and article information

                Journal
                International Journal on Document Analysis and Recognition (IJDAR)
                IJDAR
                Springer Science and Business Media LLC
                1433-2833
                1433-2825
                June 2015
                March 12 2015
                June 2015
                : 18
                : 2
                : 183-197
                Article
                10.1007/s10032-015-0242-2
                4532c915-1f51-4a05-b2fd-828c70a3ad91
                © 2015
                History

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