Improving LSTM Based Acoustic Model with Dropout Method

The deep neural network model applied on speech recognition has become one of the most successful application of deep learning. Speech recognition can be used for surgical record. Among all kinds of neural networks, recurrent neural network (RNN) is the best one for sequence modeling, because of its capacity of molding long term dependency. As a specific unit of RNN, long short-term memory (LSTM) has been widely used for speech recognition, especially for acoustic modeling. Also, when training a neural network, dropout is often used to prevent overfitting and improve model's generalization capacity. This paper explores the application of dropout with LSTM based acoustic modeling. Through combining the per-element and per-frame dropout methods, the accuracy of speech recognition is finally improved by more than 5% relatively. The experiments were done on THCHS-30 corpus.