In our recent work, we proposed a layer trajectory long short-term memory (ltLSTM) model which decouples the tasks of temporal modeling and senone classification with time-LSTMs and depth-LSTMs. The ltLSTM model achieved significant accuracy improvement over the traditional multi-layer LSTM models from our previous study. Considering the future context frames carrying valuable information for predicting the target label evidenced by the success of bi-directional LSTMs, in this work we investigate how to incorporate this kind of information with hidden vectors from either time-LSTM or depth-LSTM. Trained with 30 thousand hours of EN-US Microsoft internal data, the best ltLSTM model with future context frames can improve the baseline ltLSTM with up to 11.5% relative word error rate (WER) reduction and improve the baseline LSTM with up to 24.6% relative WER reduction across different tasks. <\/p>\n","protected":false},"excerpt":{"rendered":"
In our recent work, we proposed a layer trajectory long short-term memory (ltLSTM) model which decouples the tasks of temporal modeling and senone classification with time-LSTMs and depth-LSTMs. The ltLSTM model achieved significant accuracy improvement over the traditional multi-layer LSTM models from our previous study. Considering the future context frames carrying valuable information for predicting 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