Search Results for author: Daniel S. Park

Found 7 papers, 2 papers with code

Towards NNGP-guided Neural Architecture Search

1 code implementation11 Nov 2020 Daniel S. Park, Jaehoon Lee, Daiyi Peng, Yuan Cao, Jascha Sohl-Dickstein

Since NNGP inference provides a cheap measure of performance of a network architecture, we investigate its potential as a signal for neural architecture search (NAS).

Neural Architecture Search

Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition

no code implementations20 Oct 2020 Yu Zhang, James Qin, Daniel S. Park, Wei Han, Chung-Cheng Chiu, Ruoming Pang, Quoc V. Le, Yonghui Wu

We employ a combination of recent developments in semi-supervised learning for automatic speech recognition to obtain state-of-the-art results on LibriSpeech utilizing the unlabeled audio of the Libri-Light dataset.

 Ranked #1 on Speech Recognition on LibriSpeech test-clean (using extra training data)

Automatic Speech Recognition

Improved Noisy Student Training for Automatic Speech Recognition

no code implementations19 May 2020 Daniel S. Park, Yu Zhang, Ye Jia, Wei Han, Chung-Cheng Chiu, Bo Li, Yonghui Wu, Quoc V. Le

Noisy student training is an iterative self-training method that leverages augmentation to improve network performance.

Ranked #4 on Speech Recognition on LibriSpeech test-clean (using extra training data)

Automatic Speech Recognition Image Classification

SpecAugment on Large Scale Datasets

no code implementations11 Dec 2019 Daniel S. Park, Yu Zhang, Chung-Cheng Chiu, Youzheng Chen, Bo Li, William Chan, Quoc V. Le, Yonghui Wu

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 enhancing the performance of end-to-end networks on public datasets.

Automatic Speech Recognition

The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study

no code implementations9 May 2019 Daniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le, Samuel L. Smith

We find that the optimal SGD hyper-parameters are determined by a "normalized noise scale," which is a function of the batch size, learning rate, and initialization conditions.

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

29 code implementations18 Apr 2019 Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, Quoc V. Le

On LibriSpeech, we achieve 6. 8% WER on test-other without the use of a language model, and 5. 8% WER with shallow fusion with a language model.

Automatic Speech Recognition Data Augmentation

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