Long Short-Term Memory based Convolutional Recurrent Neural Networks for Large Vocabulary Speech Recognition
This work addresses speech recognition for large vocabulary applications, representing an incremental advancement by hybridizing established CNN and LSTM techniques.
The paper tackled the problem of large vocabulary speech recognition by proposing a convolutional recurrent neural network (CRNN) that combines CNNs and LSTM RNNs, resulting in state-of-the-art performance improvements over existing methods like FFNNs and LSTM RNNs.
Long short-term memory (LSTM) recurrent neural networks (RNNs) have been shown to give state-of-the-art performance on many speech recognition tasks, as they are able to provide the learned dynamically changing contextual window of all sequence history. On the other hand, the convolutional neural networks (CNNs) have brought significant improvements to deep feed-forward neural networks (FFNNs), as they are able to better reduce spectral variation in the input signal. In this paper, a network architecture called as convolutional recurrent neural network (CRNN) is proposed by combining the CNN and LSTM RNN. In the proposed CRNNs, each speech frame, without adjacent context frames, is organized as a number of local feature patches along the frequency axis, and then a LSTM network is performed on each feature patch along the time axis. We train and compare FFNNs, LSTM RNNs and the proposed LSTM CRNNs at various number of configurations. Experimental results show that the LSTM CRNNs can exceed state-of-the-art speech recognition performance.