# Mouth Sounds: A Review of Acoustic Applications and Methodologies
## 4 Main Findings
### Acoustic Feature Analysis
#### 4.3.1 Statistical Analyses for Evaluation  
Across studies, normality in data was primarily tested with the Kolmogorov-Smirnov test [29, 45, 74, 75, 89] and the Shapiro-Wilk test [47, 73].  
The parametric Student's t-test was reported in most studies comparing two acoustic variables, either paired or unpaired [29, 41, 45, 50, 79, 84, 85, 87, 88, 89, 104, 105, 119]. In [79], an unpaired Student's t-test was employed to analyze pathologies, such as vocal fold cysts, unilateral vocal fold paralysis, and vocal fold polyps. [PERSON]'s effect size was used in [50] to analyze pre/post-session changes in the voices of actors. The non-parametric Mann-Whitney U test for unpaired data was also reported [61, 74, 82, 94].  
Several studies conducted multiple comparisons across variables. To counteract multiple comparison issues, [PERSON]'s correction was used [50, 53, 82].  
Analysis of variance (ANOVA) and its variations was another method employed in statistical testing. Some studies used one-way ANOVA [46, 75, 83, 87, 106], two-way ANOVA [84, 97], repeated-measures ANOVA [33], multivariate ANOVA [102], [PERSON]'s test [70, 82], analysis of covariance [73], and [PERSON]'s unequal-variances \(t\)-test [65, 105].  
Correlation between variables was assessed using [PERSON]'s test [47, 75, 108, 119]. In [50], the inter-rater reliability was correlated in listening evaluations of vocal exercises. Additionally, in [82], the relationship between the cough peak flow and cough peak sound pressure level was measured using [PERSON]'s rank correlation coefficient analysis.  
Other statistical analyses presented in different works were descriptive statistics, mainly involving measures of central tendency [37, 91, 120]. These measures help to gain a better understanding of behavior and tendency. Furthermore, regression analysis was used across different studies to model relationships among variables [37, 91, 92].  
Some studies previously mentioned using combinations of statistical methods for a better inference of results based on the behavior of the analyzed signal. For example, Ref. [87] computed descriptive statistics, t-tests, and multivariate ANOVA, taking features such as shimmer, f\({}_{0}\), jitter, and NHR from the sounds of women. This procedure aimed to detect hyposalivation. Note that these techniques were used to describe relations between groups, conditions, and group interactions.  
Other studies showed the application of non-conventional statistical methods to compare features extracted from audio recordings. For example, [PERSON]'s correctedplanned comparison led to a better understanding of the effects of certain masks in acoustic features and communication [53]. Similarly, the log-likelihood function proved useful in the classification of sounds between PD sufferers and control patients, employing cross-validation leave-one-subject-out before [PERSON]'s \(t\)-test [65].  
Regarding statistical tools, the most used software was the Statistical Package for the Social Sciences [53; 62; 82; 83; 87; 88; 114], a statistical software broadly use for basic analyses.