At Clippers today, Deblin will be presenting his work on Generative Adversarial Networks that he and Adam Stiff have been working on over the summer.
Generative Adversarial Networks have been used extensively in computer vision to generate images from a noise distribution. It has been found that with conditional information, they can learn to map a source distribution to a target distribution. However, their expressive power remains untested in the domain of speech recognition.
Spectral mapping is a feature denoising technique where a model learns to predict clean speech from noisy speech. In this work, we explore the effectiveness of adversarial training on a feedforward network-based (as well as convolutional network-based) spectral mapper to predict clean speech frames from noisy context. However, we have run into some issues which we would like to share and also would like helpful comments and feedback on our future plans.