# Deep Learning Approaches for Understanding Simple Speech Commands

arXiv: https://arxiv.org/abs/1810.02364 · HTML: https://mlsanity.com/paper/1810.02364/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2018.

## Built on by (most cited first, 4 of 4)

- [Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines](https://mlsanity.com/paper/2202.08679/) (2022)
- [LCANets++: Robust Audio Classification using Multi-layer Neural Networks with Lateral Competition](https://mlsanity.com/paper/2308.12882/) (2023)
- [A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks](https://mlsanity.com/paper/2404.00076/) (2024)
- [The Art of Deception: Robust Backdoor Attack using Dynamic Stacking of Triggers](https://mlsanity.com/paper/2401.01537/) (2024)

## Abstract (arXiv)

Automatic classification of sound commands is becoming increasingly important, especially for mobile and embedded devices. Many of these devices contain both cameras and microphones, and companies that develop them would like to use the same technology for both of these classification tasks. One way of achieving this is to represent sound commands as images, and use convolutional neural networks when classifying images as well as sounds. In this paper we consider several approaches to the problem of sound classification that we applied in TensorFlow Speech Recognition Challenge organized by Google Brain team on the Kaggle platform. Here we show different representation of sounds (Wave frames, Spectrograms, Mel-Spectrograms, MFCCs) and apply several 1D and 2D convolutional neural networks in order to get the best performance. Our experiments show that we found appropriate sound representation and corresponding convolutional neural networks. As a result we achieved good classification accuracy that allowed us to finish the challenge on 8-th place among 1315 teams.