  Automatic speech recognition (ASR) systems are of vital importance nowadays
in commonplace tasks such as speech-to-text processing and language
translation. This created the need for an ASR system that can operate in
realistic crowded environments. Thus, speech enhancement is a valuable building
block in ASR systems and other applications such as hearing aids, smartphones
and teleconferencing systems. In this paper, a generative adversarial network
(GAN) based framework is investigated for the task of speech enhancement, more
specifically speech denoising of audio tracks. A new architecture based on
CasNet generator and an additional feature-based loss are incorporated to get
realistically denoised speech phonetics. Finally, the proposed framework is
shown to outperform other learning and traditional model-based speech
enhancement approaches.
