# Visualizing Deep Neural Networks for Speech Recognition with Learned Topographic Filter Maps

arXiv: https://arxiv.org/abs/1912.04067 · HTML: https://mlsanity.com/paper/1912.04067/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2019.

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

- [Interpreting intermediate convolutional layers of generative CNNs trained on waveforms](https://mlsanity.com/paper/2104.09489/) (2021)
- [Interpreting intermediate convolutional layers in unsupervised acoustic word classification](https://mlsanity.com/paper/2110.02375/) (2021)

## Abstract (arXiv)

The uninformative ordering of artificial neurons in Deep Neural Networks complicates visualizing activations in deeper layers. This is one reason why the internal structure of such models is very unintuitive. In neuroscience, activity of real brains can be visualized by highlighting active regions. Inspired by those techniques, we train a convolutional speech recognition model, where filters are arranged in a 2D grid and neighboring filters are similar to each other. We show, how those topographic filter maps visualize artificial neuron activations more intuitively. Moreover, we investigate, whether this causes phoneme-responsive neurons to be grouped in certain regions of the topographic map.