In this paper, we extend the deep long short-term memory (DLSTM) recurrent
neural networks by introducing gated direct connections between memory cells in
adjacent layers. These direct links, called highway connections, enable
unimpeded information flow across different layers and thus alleviate the
gradient vanishing problem when building deeper LSTMs. We further introduce the
latency-controlled bidirectional LSTMs (BLSTMs) which can exploit the whole
history while keeping the latency under control. Efficient algorithms are
proposed to train these novel networks using both frame and sequence
discriminative criteria. Experiments on the AMI distant speech recognition
(DSR) task indicate that we can train deeper LSTMs and achieve better
improvement from sequence training with highway LSTMs (HLSTMs). Our novel model
obtains $43.9/47.7\%$ WER on AMI (SDM) dev and eval sets, outperforming all
previous works. It beats the strong DNN and DLSTM baselines with $15.7\%$ and
$5.3\%$ relative improvement respectively.

本研究提出了一种基于深度神经网络的语音识别模型，它使用直连和双向 LSTM 来提高信息传递效率，并在 AMI DSR 任务上取得了显著的性能提升。