# Mouth Sounds: A Review of Acoustic Applications and Methodologies
## 4 Main Findings
### Applications  
#### 4.4.1 Physical Health Monitoring  
Clinical Diagnosis  
In the clinical environment, acoustical analysis has been a way to diagnose health problems [40; 79; 84; 121; 109], pathologies, and speech language complications, such as language development in children [10], those with a cleft palate [122], speech intelligibility from cochlear implant users [33], diplophonia [76], lateral signalsim [39; 101], asthma and chronic obstructive pulmonary disorder [80; 91; 93; 96], and pathologies affecting sound production through the voice (e.g., dysphonia, laryngeal changes, and hyposvalivation) [45; 72; 73; 74; 75; 76; 77; 78; 106; 119]. Technology used for voice recordings has helped to evaluate and measure progress before and after treatment. For example, early word production in children was evaluated through an app using only the internal microphone of a tablet device [13].  
In speech and language complications, acoustical features from voice recordings tend to change. The extraction of f0, CPP, and smoothed CPP were the most studied acoustic features across studies. In [45; 72; 73; 74], CPP and CPPS have shown significant differences (\(p<0.05\)) for pathology detection. Other features, such as MFCC, have shown 95% when introduced to ML algorithms, such as SVM [72; 75]. However, in diplophonia, there are two simultaneous f0; therefore, the analysis of single f0 is limited [76].  
In [87], acoustic differences between the voice of women with hyposvalivation and normal voices were explored by performing tasks, such as reading and pronuncing isolated vowels. Significant differences were observed only in isolated vowels /a/ and /i/ on f0. Therefore, the results of these studies might indicate that the frequency parameters of sound voices are useful for abnormality detection in voices.  
Besides speech, snoring has also been acoustically analyzed for clinical purposes by measuring sound intensity, formant frequencies, and spectrographic patterns before and after surgery due to nasal septal deviation [88], and describing acoustic differences in normal and drug-induced sleep [114].  
Recently, cry signals have provided relevant information about the physical and psychological states of newborns [128]. To diagnose diseases based on the analysis of the sound of a cry, in [51], a fully automatic non-invasive system using the crying sounds of babies (expiratory and inspiratory) in different contexts was developed. The signals were collected in a natural hospital environment at different times. The results achieved a high average true positive rate of approximately 95.6% for the expiratory class and 92.47% for the inspiratory class using GMM and HMM classifiers, respectively.  
In the same context, newborn cries have been correlated with negative sensations, such as hunger and discomfort [58]; on the other hand, hearing problems are related to the future development of speech disabilities [29].  
Only one study [72] used voice recordings from the MEEI Voice Disorder Database. As speech is also altered when the human body is stressed, in [38], it was proposed to extract its acoustic features to predict biological signals, such as blood volume pressure and skin conductance. The conclusion was that deep representation learning using pre-trained convolutional NN is a practical approach for blood volume pressure biological prediction [38]. Therefore, the acoustic analysis of speech can be part of a complete diagnosis and prediction of overall health.