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

      Neural Networks

      Speech Perception, Neural Networks (Computer), Models, Neurological, physiology, Memory, Computer Systems, Classification, Artificial Intelligence, Algorithms

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          Abstract

          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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          Author and article information

          Journal
          10.1016/j.neunet.2005.06.042
          16112549

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