# Fast Classification Learning with Neural Networks and Conceptors for Speech Recognition and Car Driving Maneuvers

arXiv: https://arxiv.org/abs/2102.05588 · HTML: https://mlsanity.com/paper/2102.05588/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 10 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Controlling Recurrent Neural Networks by Conceptors](https://mlsanity.com/paper/1403.3369/) (2014)
- [Open Source Automatic Speech Recognition for German](https://mlsanity.com/paper/1807.10311/) (2018)
- [Efficient Time-Series Approximation with Linear Recurrent Neural Networks: Architecture Learning and Predictive Power](https://mlsanity.com/paper/1802.03308/) (2018)
- [Conceptors: an easy introduction](https://mlsanity.com/paper/1406.2671/) (2014)

## Abstract (arXiv)

Recurrent neural networks are a powerful means in diverse applications. We show that, together with so-called conceptors, they also allow fast learning, in contrast to other deep learning methods. In addition, a relatively small number of examples suffices to train neural networks with high accuracy. We demonstrate this with two applications, namely speech recognition and detecting car driving maneuvers. We improve the state of the art by application-specific preparation techniques: For speech recognition, we use mel frequency cepstral coefficients leading to a compact representation of the frequency spectra, and detecting car driving maneuvers can be done without the commonly used polynomial interpolation, as our evaluation suggests.