Automatic speaker verification (ASV) systems are enhanced enough, that industry is attracted to use them practically in security systems. However, vulnerability of these systems to various direct and indirect access attacks weakens the power of ASV authentication mechanism. The increasing research in spoofing and anti-spoofing technologies is contributing to the enhancement of these systems. The objective of this paper is to review and analyze these important advancements proposed by different researchers and scientists. Various classical, autoregressive, cepstral, etc., and modern deep learning based feature extraction techniques that are chosen to design the frontend of these systems are discussed. Extracted features are learned and classified in the backend of an ASV system, which can be classical machine learning or deep learning models that are also the main focus of the presented review. Experimental studies use constantly modified datasets and evaluation measures to develop robust systems since emergence of practical work in this area. This paper analysis most of the contributing spoofed speech datasets and evaluation protocols. Speech synthesis (SS), voice conversion (VC), replay, mimicry and twins are the potential spoofing attacks to ASV systems. This work provides the knowledge of generation techniques of these attacks to empower the defence mechanism of ASV. This survey marks the start of a new era in ASV system development and highlights the start of a new generation (G 4 ) in SS attack development methods. With the increase in advancement of deep learning techniques, the paper makes best efforts to give the complete idea of ASV to new comers to this area and also, puts some light on some of the spoofing attacks that can be targeted during implementation of the future ASV systems.