Search Results for author:
Found 12 papers, 2 papers with code
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation
Our policy adapts the augmentation parameters based on the training loss of the data samples.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Quantized neural network design under weight capacity constraint
The complexity of deep neural network algorithms for hardware implementation can be lowered either by scaling the number of units or reducing the word-length of weights.
FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks
The weights are quantized to 6 bits to store all of them in the on-chip memory of an FPGA.
Character-Level Language Modeling with Hierarchical Recurrent Neural Networks
Recurrent neural network (RNN) based character-level language models (CLMs) are extremely useful for modeling out-of-vocabulary words by nature.
Generative Knowledge Transfer for Neural Language Models
In this paper, we propose a generative knowledge transfer technique that trains an RNN based language model (student network) using text and output probabilities generated from a previously trained RNN (teacher network).
Character-Level Incremental Speech Recognition with Recurrent Neural Networks
The output values of the CTC-trained RNN are character-level probabilities, which are processed by beam search decoding.
Online Keyword Spotting with a Character-Level Recurrent Neural Network
In this paper, we propose a context-aware keyword spotting model employing a character-level recurrent neural network (RNN) for spoken term detection in continuous speech.
Structured Pruning of Deep Convolutional Neural Networks
To decide the importance of network connections and paths, the proposed method uses a particle filtering approach.
Fixed-Point Performance Analysis of Recurrent Neural Networks
Recurrent neural networks have shown excellent performance in many applications, however they require increased complexity in hardware or software based implementations.
Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification
Our online model achieves 20. 7% phoneme error rate (PER) on the very long input sequence that is generated by concatenating all 192 utterances in the TIMIT core test set.
Resiliency of Deep Neural Networks under Quantization
In this work, the effects of retraining are analyzed for a feedforward deep neural network (FFDNN) and a convolutional neural network (CNN).
Single stream parallelization of generalized LSTM-like RNNs on a GPU
Recurrent neural networks (RNNs) have shown outstanding performance on processing sequence data.
