# Role of non-linear data processing on speech recognition task in the framework of reservoir computing

arXiv: https://arxiv.org/abs/1906.02812 · HTML: https://mlsanity.com/paper/1906.02812/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2019.

## Built on by (most cited first, 8 of 8)

- [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023)
- [Self-Contrastive Forward-Forward Algorithm](https://mlsanity.com/paper/2409.11593/) (2024)
- [Quantifying the Computational Capability of a Nanomagnetic Reservoir Computing Platform with Emergent Magnetization Dynamics](https://mlsanity.com/paper/2111.14603/) (2021)
- [Building Intelligence in the Mechanical Domain -- Harvesting the Reservoir Computing Power in Origami to Achieve Information Perception Tasks](https://mlsanity.com/paper/2302.05517/) (2023)
- [Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing](https://mlsanity.com/paper/2504.13355/) (2025)
- [Reservoir Computing with Magnetic Thin Films](https://mlsanity.com/paper/2101.12700/) (2021)
- [Neuromorphic spintronics simulated using an unconventional data-driven Thiele equation approach](https://mlsanity.com/paper/2307.09262/) (2023)
- [Quantum-activated neural reservoirs on-chip open up large hardware security models for resilient authentication](https://mlsanity.com/paper/2403.14188/) (2024)

## Abstract (arXiv)

The reservoir computing neural network architecture is widely used to test hardware systems for neuromorphic computing. One of the preferred tasks for bench-marking such devices is automatic speech recognition. However, this task requires acoustic transformations from sound waveforms with varying amplitudes to frequency domain maps that can be seen as feature extraction techniques. Depending on the conversion method, these may obscure the contribution of the neuromorphic hardware to the overall speech recognition performance. Here, we quantify and separate the contributions of the acoustic transformations and the neuromorphic hardware to the speech recognition success rate. We show that the non-linearity in the acoustic transformation plays a critical role in feature extraction. We compute the gain in word success rate provided by a reservoir computing device compared to the acoustic transformation only, and show that it is an appropriate benchmark for comparing different hardware. Finally, we experimentally and numerically quantify the impact of the different acoustic transformations for neuromorphic hardware based on magnetic nano-oscillators.