14 citations · 32 across the 4 of their papers we have counts for
8 papers
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR
Gary Wang, Ekin D. Cubuk, Andrew Rosenberg +6
Data augmentation is a ubiquitous technique used to provide robustness to automatic speech recognition (ASR) training. However, even as so much of the ASR training process has beco…
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network
William Chan, Daniel Park, Chris Lee +3
We present SpeechStew, a speech recognition model that is trained on a combination of various publicly available speech recognition datasets: AMI, Broadcast News, Common Voice, Lib…
Efficient Knowledge Distillation for RNN-Transducer Models
Sankaran Panchapagesan, Daniel S. Park, Chung-Cheng Chiu +3
Knowledge Distillation is an effective method of transferring knowledge from a large model to a smaller model. Distillation can be viewed as a type of model compression, and has pl…
Towards NNGP-guided Neural Architecture Search
Daniel S. Park, Jaehoon Lee, Daiyi Peng +2
The predictions of wide Bayesian neural networks are described by a Gaussian process, known as the Neural Network Gaussian Process (NNGP). Analytic forms for NNGP kernels are known…
Improved Noisy Student Training for Automatic Speech Recognition
Daniel S. Park, Yu Zhang, Ye Jia +5
Recently, a semi-supervised learning method known as "noisy student training" has been shown to improve image classification performance of deep networks significantly. Noisy stude…
SpecAugment on Large Scale Datasets
Daniel S. Park, Yu Zhang, Chung-Cheng Chiu +5
Recently, SpecAugment, an augmentation scheme for automatic speech recognition that acts directly on the spectrogram of input utterances, has shown to be highly effective in enhanc…