- Yuuki Tachioka, Shinji Watanabe, Jonathan Le Roux, J. Hershey. 2016. Evaluation of Noisy Speech Recognition and Sequence Discriminative Training for Low-rank Deep Neural Network Acoustic Models. Abstract: Deep neural network (DNN) acoustic models outperform conventional Gaussian mixture model (GMM) but the number of parameters tends to be larger. This leads to higher computational costs than those of GMM. To reduce DNN model size, by using singular value decomposition (SVD) have previously been applied for low-rank approximations of weight matrices. Previous studies only focused on clean speech, whereas because noisy speech is more complicated and its modeling could be difficult. Thus we investigate the effectiveness of this SVD method on noisy reverberated speech recognition task. Furthermore, we combine the low-rank approximation with sequence discriminative training, which further improved the performance of the DNN, which was constructed using a frame-by-frame discriminative criterion. We also investigated the effect of the order of application of the low-rank and sequence discriminative training. Our experiments show that low rank approximation is effective for noisy speech recognition and the most effective combination of discriminative training with model reduction is to apply the low rank approximation to the base model first and then to perform discriminative training on the low-rank model. This discriminatively low-rank trained model outperformed the low-rank discriminatively trained low-rank model.
