Generative adversarial networks for speech processing: A review. (March 2022)

• Record Type: Journal Article
• Authors: Wali, Aamir; Alamgir, Zareen; Karim, Saira; Fawaz, Ather; Ali, Mubariz Barkat; Adan, Muhammad; Mujtaba, Malik
• Is Part Of: Computer speech & language. Volume 72(2022)
• Publication Date: 2022-03
• Subjects: GANs -- Speech synthesis -- Speech enhancement -- Data augmentation -- Speech GANs; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2021.101308
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: Generative adversarial networks (GANs) have seen remarkable progress in recent years. They are used as generative models for all kinds of data such as text, images, audio, music, videos, and animations. This paper presents a comprehensive review of the novel and emerging GAN-based speech frameworks and algorithms that have revolutionized speech processing. We have categorized speech GANs based on application areas: speech synthesis, speech enhancement & conversion, and data augmentation in automatic speech recognition and emotion speech recognition systems. This review also includes a summary of the data sets and evaluation metrics commonly used in speech GANs. We also suggest some interesting research directions for future work and highlight the issues faced by current state-of-the-art speech GANs. Highlights: Presents a comprehensive review of speech GANs. Categorizes speech GANs based on application areas: speech synthesis, speech enhancement, and model augmentation. This work lays the ground for future research in speech GANs. Summarizes the evaluation metrics and resources required for conducting fruitful research.