# Speech Enhancement Algorithms: A Systematic Literature Review
###### Abstract  
A growing and pressing need for Speech Enhancement Algorithms (SEAs) has emerged with the proliferation of hearing devices and mobile devices that aim to improve speech intelligibility without sacrificing speech quality. Recently, a tremendous number of studies have been conducted in the field of speech enhancement. This study aims to map the field of speech enhancement by conducting a systematic literature review to provide comprehensive details of recently proposed SEAs. This systematic review aims to highlight research trends in SEAs and direct researchers to the most important topics published between 2015 and 2024. It attempts to address seven key research questions related to this topic. Moreover, it covers articles available in five research databases that were selected in accordance with the PRISMA protocol. Different inclusion and exclusion criteria have been performed. Across the selected databases, 47 studies met the defined inclusion criteria. A detailed explanation of SEAs in the recent literature is provided, with existing SEAs studied in a comparative fashion along with the factors influencing the choice of one over the others. This review presents different criteria related to the approaches utilized for signal modeling, the different datasets employed, types of transform-based SEAs, and the effectiveness of different measurements, among other topics. This study presents a systematic review of SEAs along with existing challenges in this field. speech enhancement; Wiener filter; MMSE; deep learning; noise; speech signal +
Footnote †: journal: _Algorithms_  
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clarity, making it a foundational technique for many later advancements in telephony and audio applications. The Wiener filter, first developed in 1940 by [PERSON], has been applied in the speech enhancement field to solve signal estimation problems for speech signals [7]. This statistical filtering method minimizes the mean squared error between the original and enhanced signal, leading to improved speech quality in noisy environments, and has found use in VoIP applications and hearing aids. In the 1980s, Kalman filters began to find applications in speech processing. First proposed in the 1960s by [PERSON] [8], the Kalman filter is a Bayesian recursive filter that can predict the optimal state of a speech signal, making it particularly useful in dynamically noisy environments such as live broadcasting and speech recognition systems. During the same period, [PERSON] (1984) introduced an algorithm for estimating the Minimum Mean Square Error (MMSE) [9]. This algorithm uses statistical modeling to suppress noise while preserving speech intelligibility, capitalizing on the major importance of the short-time spectral amplitude. This method has become a key component in hearing aids and noise suppression features in modern smart devices [9]. The 1990s saw the emergence of subspace methods [10], which decompose the speech signal into distinct components, allowing speech to be separated from noise without significantly affecting clarity. These methods have been widely used in robust speech recognition and forensic audio analysis. Additionally, statistical modeling-based methods [11] that leverage Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) to predict clean speech based on previously recorded patterns have recently gained traction. These approaches have contributed to advancements in speech synthesis, assistive voice technologies, and speech-to-text conversion. These classical SEAs laid the groundwork for modern advancements, including deep learning-based speech enhancement techniques and Generative Adversarial Networks (GANs), which build upon these fundamental principles with more sophisticated data-driven approaches to noise suppression and speech quality improvement. There are various criteria for classifying SEAs.