# Mouth Sounds: A Review of Acoustic Applications and Methodologies
## 4 Main Findings
### Respiratory Complications  
Smartphones with ML classifiers have been used to diagnose lung diseases, such as asthma and chronic obstructive pulmonary disease, by detecting forced exhalations using SVM (97% accuracy) [93] or adventitious sounds in breathing using RF (96.47% \(F\)-score) [96]. Digital devices could serve as a tool for clinicians and patients to keep track of health at home and the early detection of health complications. In the same way, in [63], an automatic system to assist clinicians in the decision-making process was developed. The system analyzed coughs and the vocalized /A:/ sounds of asthmatic and healthy children. The MFCCs with additional constant-Q cepstral coefficients features were extracted and modeled using two separate GMM-universal background models from both sounds. The fused model with both sounds showed the best results achieved a 91.1% sensitivity and 95.0% specificity. The better performance here was attributed to the larger number of cough sounds per child. To evaluate the cough ability using cough sound with or without a facemask, in [82], a cough flow prediction model associating cough sounds with cough flow was proposed. The amplitude and envelope were analyzed using different correlation methods. One of them showed that the correlation coefficients were 0.895 (\(p<0.001\); 100% power) for the in-ear mic, 0.879 (\(p<0.001\); 100% power) for the mini-speech mic, and 0.795 (\(p<0.001\); 99.9% power) for the smartphone mic. The study revealed a non-linear relationship between cough sound and cough flow. Furthermore, a feedback system for inhaler usage has been proposed to monitor subjects with asthma and chronic obstructive pulmonary disorder [80]. This system included a micro-electro-mechanical system microphone with a microcontroller to acquire acoustic breathing signals. The extracted characteristics were the MFCC, ZCR, spectral centroid, spectral spread, and energy entropy. The acoustic parameters scored high values in classification metrics with SVM (98.5% accuracy, 100% precision, 97.8% recall, and 98.9% \(F1\) score). Nevertheless, the sample consisted of six participants, and the recording condition was an indoor environment, without further details. The results open the opportunity for further research in health monitoring through acoustic signals related to respiratory diseases. Coronavirus Disease 2019 (COVID-19)  
In 2019, the world was hit by a wave of COVID-19, and the importance of knowing its impact on the health of people and daily life gained attention rapidly. Most studies focused on finding acoustic biomarkers of COVID-19 [61, 97] for its detection by means of ML algorithms [42, 43, 44, 52, 60, 62, 77, 100]. The most recurrent mouth sounds were sustained vowel /a/[42, 43, 52, 59, 61], cough sounds [43, 44, 60, 77, 100], and sentences [43, 44, 52, 59, 62]. Some authors reported using acoustic features, such as MFCC [42, 44, 52, 60, 61, 62, 63, 64, 65, 66, 77, 100] or a large combination of those mentioned in Section 2.2, as inputs for NNs [42, 43, 52, 59, 61, 77]. The Visual Geometry Group (97.2% accuracy) [100], Honey Badger Optimization-based Deep Neuro Fuzzy Network (91.76% accuracy) [77], convolutional NN (95% accuracy) [44], and feed-forward neural network (89.71% accuracy) [42] were the DL algorithms employed. However, studies pointed out the need for many acoustic characteristics (e.g., 512 [62]) to obtain accuracy scores above 80%. Interestingly, when using fewer characteristics, traditional ML methods, such as SVM and HMM-based algorithms reported 90.24% [52] and 93.33% [60] accuracy, respectively, suggesting the high performance of these methods under these conditions when data are limited. Moreover, other studies focused on the intelligibility of messages when using masks, one of the main pandemic safety measures worldwide. In [53], the effects of different masks on the voice showed no significant differences in intelligibility for single words and sentences (\(p=0.621\) and \(p=0.542\), respectively).