# Densely Connected Convolutional Networks for Speech Recognition

arXiv: https://arxiv.org/abs/1808.03570 · HTML: https://mlsanity.com/paper/1808.03570/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2018.

## Built on by (most cited first, 2 of 2)

- [Robustness of Bayesian Neural Networks to White-Box Adversarial Attacks](https://mlsanity.com/paper/2111.08591/) (2021)
- [Investigation of Densely Connected Convolutional Networks with Domain Adversarial Learning for Noise Robust Speech Recognition](https://mlsanity.com/paper/2112.10108/) (2021)

## Abstract (arXiv)

This paper presents our latest investigation on Densely Connected Convolutional Networks (DenseNets) for acoustic modelling (AM) in automatic speech recognition. DenseN-ets are very deep, compact convolutional neural networks, which have demonstrated incredible improvements over the state-of-the-art results on several data sets in computer vision. Our experimental results show that DenseNet can be used for AM significantly outperforming other neural-based models such as DNNs, CNNs, VGGs. Furthermore, results on Wall Street Journal revealed that with only a half of the training data DenseNet was able to outperform other models trained with the full data set by a large margin.