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arXiv · 1910.12620

AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks

Sherif Abdulatif·Karim Armanious·Karim Guirguis·Jayasankar T. Sajeev·Bin Yang
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Abstract

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.

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BibTeXRIS

Sherif Abdulatif, Karim Armanious, Karim Guirguis, Jayasankar T. Sajeev, Bin Yang. 2019-10-21. AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks. https://doi.org/10.23919/eusipco47968.2020.9287606

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