1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchJinyu Lihttps://www.microsoft.com/en-us/research/people/jinyli/Layer Trajectory BLSTM - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="x7g9AF4Ohz"><a href="https://www.microsoft.com/en-us/research/publication/layer-trajectory-blstm/">Layer Trajectory BLSTM</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/layer-trajectory-blstm/embed/#?secret=x7g9AF4Ohz" width="600" height="338" title="“Layer Trajectory BLSTM” — Microsoft Research" data-secret="x7g9AF4Ohz" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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Recently, we proposed layer trajectory (LT) LSTM (ltLSTM) which significantly outperforms LSTM by decoupling the functions of senone classification and temporal modeling with separate depth and time LSTMs. We further improved ltLSTM with contextual layer trajectory LSTM (cltLSTM) which uses the future context frames to predict target labels. Given bi-directional LSTM (BLSTM) also uses future […]