# Mouth Sounds: A Review of Acoustic Applications and Methodologies
## 4 Main Findings
### Respiratory Complications
Still, the intelligibility of speakers decreased depending on the type of mask used compared with using no mask. Meanwhile, in [59], the intelligibility of sentences was measured when wearing KN95 face masks against not wearing them. This study showed better intelligibility when using the mask (mean accuracy 89.38% \(\pm\) 12.39) against not using it (84.86% \(\pm\) 14.84). Nevertheless, these results could be biased by the subjective perception of the speech-language pathologist who interpreted the sentences of speakers through audio calls or by the speaking effort made by the participants. It was also noted that, following strategies such as speaking clearly, louder, and slower, the intelligibility of the message improves (94.25% \(\pm\) 12.87, 96.20% \(\pm\) 5.26, and 92.50% \(\pm\) 11.91, respectively). Neurological Disorders and Conditions  
The acoustic features found in speech recordings have helped to identify symptoms of neurological disorders, such as abnormal prosody in schizophrenia [37], depression [64], cerebral palsy with dysarthria [46], early detection of ASD in children [78], ataxia [111], observation of differences between high-risk ASD and typically developing babies [94], dementia [89], multiple [70, 85] and amyotrophic lateral sclerosis [48], and PD analysis under several conditions [47, 65, 85, 92, 104, 108, 112, 115]. The features involved in the analysis of abnormal prosody in schizophrenia were mainly the formant frequencies, involving the variability in pitch given by the standard deviation of f0, intensity, and loudness [37]. The results showed reduced variability in pitch and loudness for patients with schizophrenia compared with controls by using distributional properties and descriptive statistics. This study suggested that symptoms of aprosody may be quantified by acoustical analysis. For instance, in [78], vocalization recordings in children while reading words, sentences, and stories were used to detect ASD. A correlation-based feature-subset-selection algorithm selected paramount acoustic features out of 74. These features were demonstrated to be age- and gender-independent. Parameters. such as the root-mean-square energy, MFCC, ZCR, probability of voicing, f0, and the first-order delta coefficients, of these elements combined with visual facial attributes have achieved significant results in classifying voice features between high-risk ASD and typically developing babies. SVM was the algorithm with the best results observed in the metrics of the classifier (96.39% accuracy, 95.00% recall, 97.67% specificity, 94.59% area under the curve, and 97.44% positive predictive value) [94]. Another example refers to dementia detection. Promising results by studying pitch, overall intonation, differences between harmonics, jitter, shimmer, HNR and NHR, creakiness, and breathiness were reported in [89]. SVM with selected features by recursive feature elimination showed 100% accuracy. Features were extracted from manually transcribed conversations between neurologists and patients. Regarding types of sclerosis, significant differences (\(p<0.05\)) were observed in jitter, f0, shimmer, NHR, and the formal centralization ratio for the detection of multiple sclerosis [70; 85]. Furthermore, linear discriminant analysis reported 88% accuracy when vowel features, such as differences between envelopes, mutual formant elements, and harmonic amplitude structure, were introduced to the classifier. The most investigated acoustic parameters across studies concerning PD were f0, jitter, shimmer, range, and intensity [47; 85; 104; 108]. Some studies showed significant evidence for voice anomaly detection related to PD (\(p<0.05\)) [85; 104] or 99% accuracy using SVM [47].