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SpecAugment: A New Data Augmentation Method for Automatic Speech Recognition

Monday, April 22, 2019

Posted by Daniel S. Park, AI Resident and William Chan, Research Scientistthe ongoing development of deep neural networksdata augmentationimage classificationSpecAugment: A Simple Data Augmentation Method for Automatic Speech RecognitionLibriSpeech 960hSwitchboard 300hSpecAugmentspectrogram

A waveform is typically converted into a visual representation (in our case, a log mel spectrogram; steps 1 through 3 of this article) before being fed into a network.

warping

The log mel spectrogram is augmented by warping in the time direction, and masking (multiple) blocks of consecutive time steps (vertical masks) and mel frequency channels (horizontal masks). The masked portion of the spectrogram is displayed in purple for emphasis.

Listen Attend and Spell (LAS) networksWord Error Rate

Performance of networks on the test sets of LibriSpeech with and without augmentation. The LibriSpeech test set is divided into two portions, test-clean and test-other, the latter of which contains noisier audio data.

Training, clean (dev-clean) and noisy (dev-other) development set performance with and without augmentation.

State-of-the-Art ResultsLibriSpeech 960hSwitchboard 300h

Word error rates (%) for state-of-the-art results for the tasks LibriSpeech 960h and Switchboard 300h. The test set for both tasks have a clean (clean/Switchboard) and a noisy (other/CallHome) subset. Previous SOTA results taken from Li et. al (2019), Yang et. al (2018) and Zeyer et. al (2018).

Performance of various classes of networks on LibriSpeech and Switchboard tasks. The performance of LAS models is compared to classical (e.g., HMM) and other end-to-end models (e.g., CTC/ASG) over time.

Language ModelsLanguage models

Word error rates for LibriSpeech and Switchboard tasks with and without LMs. SpecAugment outperforms previous state-of-the-art even before the inclusion of a language model.

AcknowledgementsWe would like to thank the co-authors of our paper Chung-Cheng Chiu, Ekin Dogus Cubuk, Quoc Le, Yu Zhang and Barret Zoph. We also thank Yuan Cao, Ciprian Chelba, Kazuki Irie, Ye Jia, Anjuli Kannan, Patrick Nguyen, Vijay Peddinti, Rohit Prabhavalkar, Yonghui Wu and Shuyuan Zhang for useful discussions.