QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions
Audio and Speech Processing
2019-10-24 v1
Abstract
We propose a new end-to-end neural acoustic model for automatic speech recognition. The model is composed of multiple blocks with residual connections between them. Each block consists of one or more modules with 1D time-channel separable convolutional layers, batch normalization, and ReLU layers. It is trained with CTC loss. The proposed network achieves near state-of-the-art accuracy on LibriSpeech and Wall Street Journal, while having fewer parameters than all competing models. We also demonstrate that this model can be effectively fine-tuned on new datasets.
Cite
@article{arxiv.1910.10261,
title = {QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions},
author = {Samuel Kriman and Stanislav Beliaev and Boris Ginsburg and Jocelyn Huang and Oleksii Kuchaiev and Vitaly Lavrukhin and Ryan Leary and Jason Li and Yang Zhang},
journal= {arXiv preprint arXiv:1910.10261},
year = {2019}
}
Comments
Submitted to ICASSP 2020