Editing State-of-the-art (section)

==== Donahue, C., Li, B., & Prabhavalkar, R. (2018). ''Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition'' (arXiv:1711.05747). arXiv. <nowiki>http://arxiv.org/abs/1711.05747</nowiki> ====
* '''Summary:''' This paper investigates the application of Generative Adversarial Networks (GANs) for speech enhancement, particularly for improving the noise robustness of ASR systems. Through comprehensive experiments, it introduces a frequency-domain approach (FSEGAN) to speech enhancement that shows improved ASR performance over traditional time-domain methods (SEGAN).
* '''RQ:''' Can GAN-based speech enhancement techniques effectively improve the noise robustness of ASR systems compared to traditional noise suppression methods?
* '''Hypothesis:''' The paper hypothesizes that GAN-based speech enhancement, especially when operating on log-Mel filterbank spectra rather than waveforms, will provide significant improvements in ASR system performance in noisy conditions.
* '''Conclusion:''' The study concludes that while GAN-based speech enhancement methods, particularly FSEGAN, can improve ASR performance in noisy conditions, they do not outperform multi-style training (MTR) methods. Retraining the ASR system using both the original noisy audio and the audio improved by GANs leads to better performance. This suggests that GAN-enhanced audio could be a valuable addition to improve ASR systems when used alongside the original noisy input.
* '''Critical observations:''' SEGAN, while effective in removing additive noise, is less effective in reverberant conditions compared to the frequency-domain approach (FSEGAN). On the contrast, FSEGAN significantly improves ASR performance but does not outperform traditional MTR alone. However, combining noisy and enhanced features for retraining enhances the system's robustness.
* '''Relevance:''' This article is relevant to techniques used to bolster the performance of ASR systems, highlighting the significant potential of innovating GAN-based model in this field.