Perceptual Speech Enhancement via Generative Adversarial Networks
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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 of an ASR system that can operate in realistic crowded environments. Thus, speech enhancement is now considered as a fundamental building block in newly developed ASR systems. In this paper, a generative adversarial network (GAN) based framework is investigated for the task of speech enhancement of audio tracks. A new architecture based on CasNet generator and additional perceptual loss is incorporated to get realistically denoised speech phonetics. Finally, the proposed framework is shown to quantitatively outperform other GAN-based speech enhancement approaches.
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