  Long Short-Term Memory (LSTM) is the primary recurrent neural networks
architecture for acoustic modeling in automatic speech recognition systems.
Residual learning is an efficient method to help neural networks converge
easier and faster. In this paper, we propose several types of residual LSTM
methods for our acoustic modeling. Our experiments indicate that, compared with
classic LSTM, our architecture shows more than 8% relative reduction in Phone
Error Rate (PER) on TIMIT tasks. At the same time, our residual fast LSTM
approach shows 4% relative reduction in PER on the same task. Besides, we find
that all this architecture could have good results on THCHS-30, Librispeech and
Switchboard corpora.
