3.6k citations · 8k across the 37 of their papers we have counts for
8 papers · 1 filter
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…
An Empirical Study of Generation Order for Machine Translation
William Chan, Mitchell Stern, Jamie Kiros +1
In this work, we present an empirical study of generation order for machine translation. Building on recent advances in insertion-based modeling, we first introduce a soft order-re…
Big Bidirectional Insertion Representations for Documents
Lala Li, William Chan
The Insertion Transformer is well suited for long form text generation due to its parallel generation capabilities, requiring generation steps to generate tokens.…
Speaker-Targeted Audio-Visual Models for Speech Recognition in Cocktail-Party Environments
Guan-Lin Chao, William Chan, Ian Lane
Speech recognition in cocktail-party environments remains a significant challenge for state-of-the-art speech recognition systems, as it is extremely difficult to extract an acoust…
KERMIT: Generative Insertion-Based Modeling for Sequences
William Chan, Nikita Kitaev, Kelvin Guu +2
We present KERMIT, a simple insertion-based approach to generative modeling for sequences and sequence pairs. KERMIT models the joint distribution and its decompositions (i.e., mar…
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
Daniel S. Park, William Chan, Yu Zhang +4
We present SpecAugment, a simple data augmentation method for speech recognition. SpecAugment is applied directly to the feature inputs of a neural network (i.e., filter bank coeff…