Feature enhancement with a Reservoir-based Denoising Auto Encoder

Recently, automatic speech recognition has advanced significantly by the introduction of deep neural networks for acoustic modeling. However, there is no clear evidence yet that this does not come at the price of less generalization to conditions that were not present during training. On the other hand, acoustic modeling with Reservoir Computing (RC) did not offer improved clean speech recognition but it leads to good robustness against noise and channel distortions. In this paper, the aim is to establish whether adding feature denoising in the front-end can further improve the robustness of an RC-based recognizer, and if so, whether one can devise an RC-based Denoising Auto Encoder that outperforms a traditional denoiser like the ETSI Advanced Front-End. In order to answer these questions, experiments are conducted on the Aurora-2 benchmark.