文章基本信息

标题：On Improvement of Speech Intelligibility and Quality: A Survey of Unsupervised Single Channel Speech Enhancement Algorithms
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作者：Elena Verdú ; Nasir Saleem ; Muhammad Irfan Khattak 等
期刊名称：International Journal of Interactive Multimedia and Artificial Intelligence
印刷版ISSN：1989-1660
出版年度：2020
卷号：6
期号：2
页码：12
DOI：10.9781/ijimai.2019.12.001
语种：English
出版社：ImaI-Software
摘要：Many forms of human communication exist; for instance, text and nonverbal based. Speech is, however, the most powerful and dexterous form for the humans. Speech signals enable humans to communicate and this usefulness of the speech signals has led to a variety of speech processing applications. Successful use of these applications is, however, significantly aggravated in presence of the background noise distortions. These noise signals overlap and mask the target speech signals. To deal with these overlapping background noise distortions, a speech enhancement algorithm at front end is crucial in order to make noisy speech intelligible and pleasant. Speech enhancement has become a very important research and engineering problem for the last couple of decades. In this paper, we present an all-inclusive survey on unsupervised single-channel speech enhancement (U-SCSE) algorithms. A taxonomy based review of the U-SCSE algorithms is presented and the associated studies regarding improving the intelligibility and quality are outlined. The studies on the speech enhancement algorithms in unsupervised perspective are presented. Objective experiments have been performed to evaluate the potential of the U-SCSE algorithms in terms of improving the speech intelligibility and quality. It is found that unsupervised speech enhancement improves the speech quality but the speech intelligibility improvement is deprived. To finish, several research problems are identified that require further research.