ArticlePeerJ. Computer science2025
Multichannel speech enhancement for automatic speech recognition: a literature review.
Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Multichannel speech enhancement (MCSE) is crucial for improving the robustness and accuracy of automatic speech recognition (ASR) systems. Due to the importance of ASR systems, extensive research has been conducted in MCSE, leading to rapid advancements in methods, models, and datasets. Most previous reviews point to the lack of a systematic literature review of MCSE for ASR systems. This systematic literature review aims to (1) perform a comprehensive review of the existing approaches in MCSE for ASR, (2) analyze the performance of the MCSE and ASR for various techniques, models, as well as noise data and environments, and (3) discuss the challenges, limitations, and future research directions in this research area. We conducted keyword searches on several electronic databases such as Google Scholar, IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and ISI Web of Knowledge to identify relevant journal and conference articles. We selected 240 articles based on inclusion criteria from the initial search results and ended with 35 experimental articles when exclusion criteria were applied. Through backward snowballing and the quality assessment, the final tally was 40 articles, comprising 23 journals, and 17 conference articles. The review shows that there is an increasing trend in MCSE for ASR with word error rate (WER), perceptual evaluation of speech quality (PESQ), and short-time objective intelligence (STOI) as common forms of performance measures. One of the major issues that we found in the review is the generality and comparability of the MCSE works, making it difficult to come up with unified solutions to noises in speech recognition. This systematic literature review has extensively examined MCSE and ASR techniques. Key findings include identifying MCSE methods that help ASR performance across various models, techniques, noise, and environments. We also identify several key areas researchers can explore in the future due to their promising potential.
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