Current speech enhancement techniques operate on the spectral domain and/or
exploit some higher-level feature. The majority of them tackle a limited number
of noise conditions and rely on first-order statistics. To circumvent these
issues, deep networks are being increasingly used, thanks to their ability to
learn complex functions from large example sets. In this work, we propose the
use of generative adversarial networks for speech enhancement. In contrast to
current techniques, we operate at the waveform level, training the model
end-to-end, and incorporate 28 speakers and 40 different noise conditions into
the same model, such that model parameters are shared across them. We evaluate
the proposed model using an independent, unseen test set with two speakers and
20 alternative noise conditions. The enhanced samples confirm the viability of
the proposed model, and both objective and subjective evaluations confirm the
effectiveness of it. With that, we open the exploration of generative
architectures for speech enhancement, which may progressively incorporate
further speech-centric design choices to improve their performance.

本研究提出使用生成对抗网络进行语音增强，通过训练模型使其在波形级别对 28 个说话人和 40 种不同噪声条件进行增强，目的是解决当前技术只能处理少量特定噪声条件和依赖于一阶统计量的问题，实验证明该模型的可行性和有效性，未来可以进一步探索生成性结构用于提高语音增强的性能。