Automatic Speech Recognition: Systematic Literature Review

TLDR

Speech signal processing has seen extensive research, with growing interest in automatic speech recognition (ASR) evolving from simple limited‑sound systems to sophisticated natural language–responsive systems. The review aims to guide researchers by highlighting key topics from the past six years, identifying major real‑world ASR challenges, and outlining current research gaps. The authors performed a systematic review following PRISMA guidelines, searching five databases for ASR studies published between 2015 and 2020. The review identified 45 relevant ASR studies from 2015–2020, revealing current research trends and pointing to new directions.

Abstract

A huge amount of research has been done in the field of speech signal processing in recent years. In particular, there has been increasing interest in the automatic speech recognition (ASR) technology field. ASR began with simple systems that responded to a limited number of sounds and has evolved into sophisticated systems that respond fluently to natural language. This systematic review of automatic speech recognition is provided to help other researchers with the most significant topics published in the last six years. This research will also help in identifying recent major ASR challenges in real-world environments. In addition, it discusses current research gaps in ASR. This review covers articles available in five research databases that were completed according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. The search strategy yielded 45 articles related to the study's scope for the period 2015–2020. The results presented in this review shed light on research trends in the area of ASR and also suggest new research directions.