# Speech Enhancement Algorithms: A Systematic Literature Review
## 4 Results and Discussion
Datasets mentioned in the reviewed papers include CHiME-II and CHiME-III, which were used in [51] to compare the performance of algorithms in various noise environments. CHiME-4 was used to evaluate the one-channel test in [80]. Aurora-4 was used in [51] to assess system performance in various noisy environments. Other datasets adopted in the reviewed studies include SPANZ [70], BKB [70], VCTK Corpus, which provides diverse English accents for speech enhancement tasks [61], QUT [61], VoiceBank-DEMAND [94], IEEE Speech [96], RSG-10(Noise), which provides random signal generation noise [60], WSJO Corpus, AMI Corpus, and ADOS Corpus [54]. These datasets are frequently used in SEA research. They offer a variety of settings for the testing and training phases of speech enhancement algorithms. Synthetic noise datasets are often used to assess model performance and replicate difficult acoustic conditions. In future studies, larger and more varied datasets could be used to improve robustness and generalizability. **RQ6: What are the effective measurements for evaluating speech intelligibility and quality? **  
It is necessary to assess SEA performance based on effectiveness metrics that provide meaningful and reliable procedures. The analyzed studies show that two principal criteria are used to measure the goodness of speech signals, namely, quality and intelligibility. Speech quality deals with speech clarity, nature of distortion, and amount of background noise, while speech intelligibility deals with the percentage of words that can be clearly understood. Good quality does not always guarantee high intelligibility, as they are independent of each other. Therefore, most SEAs improve quality at the expense of reducing intelligibility [40; 106]. In general, there are two categories of speech quality assessment, namely, subjective and objective measures. Objective measurements are preferred, since they are dependent on a mathematical notion. Many metrics are commonly used in SEA evaluation, and every metric has its own strengths and weaknesses. Perceptual Evaluation of Speech Quality (PESQ) is considered the most important objective measure based on the reviewed studies. A total of 30 out of 47 studies used PESQ, constituting a majority of the reviewed papers. Table 10 illustrates the most commonly used measurements, their abbreviations, a brief description, their percentage of use in the reviewed works, and which studies used them. The measurements mentioned in Table 10 can be considered the most widely used in the field of speech enhancement. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Measurements** & **Description** & **Percentage** & **References** \\ \hline PESQ (Perceptual Evaluation of Speech Quality) & A very important objective measure. It predicts the subjective Mean Opinion Score, and its score is mapped to a MOS-like scale that is regarded an accurate estimation measure available recently. & (11 out of 47) = 63\% & [1; 51; 53; 56; 60; 61; 63; 64; 69; 70; 72; 75; 76; 77; 78; 80; 84; 86; 87; 88; 91; 92; 93; 94; 95; 96; 97] & \\ \hline SNR (Signal to Noise ratio) & This is a commonly used metric for evaluating SEA that measures the ratio of speech signal power to noise signal power. A higher SNR ratio indicates better performance. & (11 out of 47) = 23\% & [3; 57; 63; 64; 66; 67; 71; 75; 88; 90] \\ \hline STOI (Short-Time Objective Intelligibility) & Establishes high correlations with intelligibility of noisy and time–frequency weighted noisy speech. & (12 out of 47) = 25\% & [51; 60; 61; 63; 70; 72; 77; 78; 80; 86; 93; 96] \\ \hline The composite measures, which are: a- SIG (Signal Distortion), b- BAK (Background Noise), c-OVL (Overall Quality). & A set of objective quality measures used to assess speech quality and overall effectiveness of SEA. SIG: to measure signal distortion. BAK: to measure noise distortion. OVL: to measure overall quality. & (3 out of 47) = 6\% & [3; 94; 97] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Measurements and their descriptions.