# End-to-end Keyword Spotting using Xception-1d

arXiv: https://arxiv.org/abs/2110.07498 · HTML: https://mlsanity.com/paper/2110.07498/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 9 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Reinforcement Learning with Unsupervised Auxiliary Tasks](https://mlsanity.com/paper/1611.05397/) (2016)
- [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation](https://mlsanity.com/paper/1901.02985/) (2019)
- [Hello Edge: Keyword Spotting on Microcontrollers](https://mlsanity.com/paper/1711.07128/) (2017)
- [A neural attention model for speech command recognition](https://mlsanity.com/paper/1808.08929/) (2018)
- [Depthwise Convolution is All You Need for Learning Multiple Visual Domains](https://mlsanity.com/paper/1902.00927/) (2019)
- [Listening to the World Improves Speech Command Recognition](https://mlsanity.com/paper/1710.08377/) (2017)
- [The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing](https://mlsanity.com/paper/1805.03305/) (2018)

## Abstract (arXiv)

The field of conversational agents is growing fast and there is an increasing need for algorithms that enhance natural interaction. In this work we show how we achieved state of the art results in the Keyword Spotting field by adapting and tweaking the Xception algorithm, which achieved outstanding results in several computer vision tasks. We obtained about 96% accuracy when classifying audio clips belonging to 35 different categories, beating human annotation at the most complex tasks proposed.