This paper proposes a new regularization algorithm referred to as macro-block dropout. The overfitting issue has been a difficult problem in training large neural network models. The dropout technique has proven to be simple yet very effective for regularization by preventing complex co-adaptations during training. In our work, we define a macro-block that contains a large number of units from the input to a Recurrent Neural Network (RNN). Rather than applying dropout to each unit, we apply random dropout to each macro-block. This algorithm has the effect of applying different drop out rates for each layer even if we keep a constant average dropout rate, which has better regularization effects. In our experiments using Recurrent Neural Network-Transducer (RNN-T), this algorithm shows relatively 4.30 % and 6.13 % Word Error Rates (WERs) improvement over the conventional dropout on LibriSpeech test-clean and test-other. With an Attention-based Encoder-Decoder (AED) model, this algorithm shows relatively 4.36 % and 5.85 % WERs improvement over the conventional dropout on the same test sets.

The stochastic gradient descent (SGD) method is most widely used for deep neural network (DNN) training. However, the method does not always converge to a flat minimum of the loss surface that can demonstrate high generalization capability. Weight noise injection has been extensively studied for finding flat minima using the SGD method. We devise a new weight-noise injection-based SGD method that adds symmetrical noises to the DNN weights. The training with symmetrical noise evaluates the loss surface at two adjacent points, by which convergence to sharp minima can be avoided. Fixed-magnitude symmetric noises are added to minimize training instability. The proposed method is compared with the conventional SGD method and previous weight-noise injection algorithms using convolutional neural networks for image classification. Particularly, performance improvements in large batch training are demonstrated. This method shows superior performance compared with conventional SGD and weight-noise injection methods regardless of the batch-size and learning rate scheduling algorithms.

Real-time automatic speech recognition (ASR) on mobile and embedded devices has been of great interests for many years. We present real-time speech recognition on smartphones or embedded systems by employing recurrent neural network (RNN) based acoustic models, RNN based language models, and beam-search decoding. The acoustic model is end-to-end trained with connectionist temporal classification (CTC) loss. The RNN implementation on embedded devices can suffer from excessive DRAM accesses because the parameter size of a neural network usually exceeds that of the cache memory and the parameters are used only once for each time step. To remedy this problem, we employ a multi-time step parallelization approach that computes multiple output samples at a time with the parameters fetched from the DRAM. Since the number of DRAM accesses can be reduced in proportion to the number of parallelization steps, we can achieve a high processing speed. However, conventional RNNs, such as long short-term memory (LSTM) or gated recurrent unit (GRU), do not permit multi-time step parallelization. We construct an acoustic model by combining simple recurrent units (SRUs) and depth-wise 1-dimensional convolution layers for multi-time step parallelization. Both the character and word piece models are developed for acoustic modeling, and the corresponding RNN based language models are used for beam search decoding. We achieve a competitive WER for WSJ corpus using the entire model size of around 15MB and achieve real-time speed using only a single core ARM without GPU or special hardware.

Real-time automatic speech recognition (ASR) on mobile and embedded devices has been of great interests for many years. We present real-time speech recognition on smartphones or embedded systems by employing recurrent neural network (RNN) based acoustic models, RNN based language models, and beam-search decoding. The acoustic model is end-to-end trained with connectionist temporal classification (CTC) loss. The RNN implementation on embedded devices can suffer from excessive DRAM accesses because the parameter size of a neural network usually exceeds that of the cache memory and the parameters are used only once for each time step. To remedy this problem, we employ a multi-time step parallelization approach that computes multiple output samples at a time with the parameters fetched from the DRAM. Since the number of DRAM accesses can be reduced in proportion to the number of parallelization steps, we can achieve a high processing speed. However, conventional RNNs, such as long short-term memory (LSTM) or gated recurrent unit (GRU), do not permit multi-time step parallelization. We construct an acoustic model by combining simple recurrent units (SRUs) and depth-wise 1-dimensional convolution layers for multi-time step parallelization. Both the character and word piece models are developed for acoustic modeling, and the corresponding RNN based language models are used for beam search decoding. We achieve a competitive WER for WSJ corpus using the entire model size of around 15MB and achieve real-time speed using only a single core ARM without GPU or special hardware.