Towards the Objective Speech Assessment of Smoking Status based on Voice Features: A Review of the Literature

Effect of Smoking on Cepstral Parameters

Smoking exerts certain damage to the voice, which affects sound characteristics. This study explored the effects of smoking, smoking time, and smoking amount on cepstral parameters. We collected the acoustic signals of sustained vowels in 301 participants (135 smokers and 166 nonsmokers). The cepstral parameters, including cepstral peak prominence (CPP), CPP standard deviation (CPP SD), low to high-frequency spectral ratio (L/H), low to high-frequency spectral ratio standard deviation (L/H SD), and voice disorder cepstral/spectral index of dysphonia (CSID), of the sustained vowels were investigated through the analysis of dysphonia in speech and voice (ADSV) application. The effects of smoking on these parameters were explored. The influences of smoking time and smoking amount on cepstral parameters were also analyzed by multiple linear regression. The CPP and L/H values in the smoking group were lower than those in the nonsmoking group (CPP: P < 0.001, L/H: P = 0.033) and negatively correlated with smoking time (CPP: R2 = 0.3828, P < 0.0001; L/H: R2 = 0.02996, P = 0.0447) and smoking amount (CPP: R2 < 0.4526, P < 0.0001; L/H: R2 = 0.08823, P = 0.00005). The CPP SD, L/H SD, and CSID values in the smoking group were higher than those in the nonsmoking group (CPP SD: P = 0.006, L/H SD: P = 0.034, CSID: P < 0.001) and positively correlated with smoking time (CPP SD: R2 = 0.03648, P = 0.0265, L/H SD: R2 = 0.09121, P = 0.0004, CSID: R2 = 0.01247, P = 0.1972) and smoking amount (CPP SD: R2 = 0.05495, P = 0.0062, L/H SD: R2 = 0.1316, P < 0.0001, CSID: R2 = 0.03851, P = 0.0225). Compared with other cepstral parameters, smoking time and smoking volume had the greatest impact on CPP (smoking time: R2 = 0.385, smoking amount: R2 = 0.443). This study confirmed that smoking has a significant effect on cepstral parameters. Compared with the cepstral parameters of nonsmokers, an increase in smoking time and smoking volume increases the abnormality of the cepstral parameters of smokers.

Analysis of COVID-19 Resulting Cough Using Formants and Automatic Speech Recognition System

As part of our contributions to researches on the ongoing COVID-19 pandemic worldwide, we have studied the cough changes to the infected people based on the Hidden Markov Model (HMM) speech recognition classification, formants frequency and pitch analysis. In this paper, An HMM-based cough recognition system was implemented with 5 HMM states, 8 Gaussian Mixture Distributions (GMMs) and 13 dimensions of the basic Mel-Frequency Cepstral Coefficients (MFCC) with 39 dimensions of the overall feature vector. A comparison between formants frequency and pitch extracted values is realized based on the cough of COVID-19 infected people and healthy ones to confirm our cough recognition system results. The experimental results present that the difference between the recognition rates of infected and non-infected people is 6.7%. Whereas, the formant analysis variation based on the cough of infected and non-infected people is clearly observed with F1, F3, and F4 and lower for F0 and F2.