# Speech Enhancement Algorithms: A Systematic Literature Review
## 4 Results and Discussion
**RQ7: What are the current limitations and challenges in the reviewed SEAs? **  
This section reviews the state-of-the-art approaches and previous findings related to speech enhancement problems based on the retained papers. Regardless of the successful SEA applications in various fields, many gaps and limitations still exist that should be addressed. The main concern of this research is focused on finding the significant issues that still need to be handled. The significant limitation that has been addressed in the retained papers is focusing on randomness of the signal, which has a great impact on subsequent speech and noise modeling. The nonstationarity of signals affects the statistical modeling of speech and noise signals [1; 78; 97]. Therefore, the noise reduction performance of many SEAs depends on noise estimation and signal modeling, especially in complex environments [61; 83; 96]. This issue is also related to another challenge caused by the surrounding environment in the form of simultaneous conversations and overlapping speech. This problem is termed the \"cocktail party problem\", and happens when two or more people are talking at the same time [48]. Data scarcity is another issue addressed in the current research. Using shared datasets is a cost-effective and feasible way to gradually advance a research field, allowing results to be analyzed and improved. The limited public availability of real-world noisy datasets for robust model training can affect the performance of SEAs in general [56; 94]. Typically, SEAs work well for enhancing speech quality but poorly for enhancing intelligibility. Different articles show a tradeoff between quality and intelligibility when seeking to improve the overall performance and accuracy of the entire SEA system; therefore, some works tend to enhance both properties together [1; 51; 3]. SEAs based on single-channel microphone environments aim to enhance the magnitude component in the time-frequency (TF) domain. On the other hand, the phase component of the input noisy signal is used without any processing. Accordingly, sound quality is damaged due to the mismatch between the estimated magnitude and the unprocessed phase spectra, causing perceptually disturbing artifacts that are sometimes referred to as phase mismatches [51; 56; 72]. In general, SEAs based on neural networks aim to learn a noisy-to-clean transformation based on a supervised learning principle. Nevertheless, the trained networks may not be effective at handling the signal, and types of noise that were not present in the training data can cause \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Measurements** & **Description** & **Percentage** & **References** \\ \hline MOS-LQO (Mean Objective) & Opinion Quality & It utilizes a spectro-temporal measure of similarity between a reference signal and a test signal to produce a MOS-LQO score that is ranged from 1 (the worst) to 5 (the best). & (2 out of 47) = 4\% & [61; 63] \\ \hline SI-SNR (scale-invariant SNR). & It evaluates speech enhancement and the level of distortion in the processed signal is measured by comparison with the reference signal. & (12 out of 47) = 25\% & [63; 70; 80] \\ \hline SegSNR (segmental signal-to-noise ratio) & Measures the frame-based segmental signal-to-noise ratio (SNR), and averages SNR measurements over short frames. It is a reasonable measure of speech quality. & (12 out of 47) = 25\% & [1; 15; 56; 72; 76; 79; 88; 91; 92; 93; 95; 96; 97] \\ \hline SDR (Signal-to-Distortion Ratio) and its version SI_SDR (Scale-Invariant SDR) & It is used to measure signal quality in comparison to its distorted or noisy version. & (5 out of 47) = 10\% & [51; 53; 63; 80; 84] \\ \hline MOS (The subjective Mean Opinion Score) & It is a well-known speech quality assessment in terms of human perception is the mean opinion score of human subjective ratings. & (2 out of 47) = 4\% & [63; 88] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._domain mismatch between the training and test sets [107].