Recent Trends in Application of Neural Networks to Speech Recognition

: In this paper, we review the research work that deal with neural network based speech recognition and the various approaches they take to bring in accuracy. Three approaches of speech recognition using neural network learning models are discussed: (1) Deep Neural Network(DNN) - Hidden Markov Model(HMM), (2) Recurrent Neural Networks(RNN) and (3) Long Short Term Memory(LSTM). It also discusses how for a given application one model is better suited than the other and when should one prefer one model over another.A pre-trained Deep Neural Network - Hidden Markov Model hybrid architecture trains the DNN to produce a distribution over tied triphone states as its output. The DNN pre-training algorithm is a robust and often a helpful way to initialize deep neural networks generatively that can aid in optimization and reduce generalization error. Combining recurrent neural nets and HMM results in a highly discriminative system with warping capabilities. To evaluate the impact of recurrent connections we compare the train and test characteristic error rates of DNN, Recurrent Dynamic Neural Networks (RDNN), and Bi-Directional Deep Neural Network (BRDNN) models while roughly controlling for the total number of free parameters in the model. Both variants of recurrent models show substantial test set characteristic error rate improvements over the non-recurrent DNN model. Inspired from the discussion about how to construct deep RNNs, several alternative architectures were constructed for deep LSTM networks from three points: (1) input-to-hidden function, (2) hidden-to-hidden transition and (3) hidden-to-output function. Furthermore, some deeper variants of LSTMs were also designed by combining different points