## 6 Conclusions  
In the present study, seven NSA features showed different trends for different voice types. The spectral envelopes of the NSA signals were notably different for each voice type. The breathy voice spectral envelope decreased sharply from \(H_{1}\)-\(H_{4}\). The modal voice spectral envelope also decreased from \(H_{1}\)-\(H_{4}\), but less sharply than that of the breathy voice. The pressed voice spectral envelope increased from \(H_{1}\)-\(H_{2}\) before decreasing from \(H_{2}\)-\(H_{4}\). The SE revealed a notable difference between the pressed voice type and the other two types. The pressed voice had a much greater average SE (approximately 90% greater) than \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **DT** & **LD** & **SVM** & **KNN** & **NN** & **Per-Set** \\ \hline Full Set & 77.4 & 78.3 & 81.3 & 81.0 & 82.5 & 80.1 \(\pm\) 2.1 \\ \(H_{1-4}\) + SE & 73.5 & 75.2 & 74.9 & 75.1 & 76.7 & 75.1 \(\pm\) 1.1 \\ \(J_{r}\) + \(S_{r}\) & 59.8 & 60.0 & 59.0 & 57.1 & 61.5 & 59.5 \(\pm\) 1.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overall classification accuracies (%) for the full set, the spectral set and the stability set. Figure 8: Per-type classification accuracies for the spectral set and the stability set. The red dashed line represents that averaged accuracy on a dataset using different classifiers. the other two types of voice. No notable difference between different voice types was found in jitter. The shimmer was the lowest for modal voice, which also had the smallest aggregation. The use of machine learning methods improved the voice type classification accuracies by approximately 20% from 60%-80% compared with the single-feature classification method. In particular, the pressed voice classification accuracy was improved by 13.1%. \(S_{r}\) and SE were found to make the greatest contribution to the machine learning classification. Increased sample size and auditory perceptual screening contributed to reducing the standard deviation of features and improved the differentiation accuracy between modal and pressed voice types. The main contribution of this study was the successful application of machine learning algorithms to the NSA data for voice type classification. An overall classification accuracy of 82.5% was achieved. The approach proposed here is based on a noninvasive and portable measurement tool, i.e., the NSA, which offers long-term voice data recording capability and speaker privacy preservation. This work may lead to clinical applications. It could assist in the measurement of vocal fatigue and determine voice safety limits by incorporating the voice type factor into the vocal dose measures [35]. Considering that the existing vocal dose metrics cannot perfectly represent the mechanical manipulation and tissue strain exhibited by the VFs in isolation, analysis of voicing type information could complement existing vocal dose metrics as a surrogate of VF contact area and open quotient in voice use quantification [36]. One other plausible application is to integrate machine learning algorithms with existing clinical practices for the evaluation of voice quality and function, creating a hybrid expert system of voice assessment by incorporating other factors, such as subjective reports on vocal conditions. Such an expert system will help further understand the mechanisms of VF injury and repair and assist voice pathology medical diagnostics. One study limitation is that diphthongs and voiced consonants were excluded from the analysis. They should be accounted for as additional voiced speech sounds in English. A comprehensive speech analysis is not possible unless an investigation of diphthongs and voiced consonants is completed. Future work should include the application of machine learning techniques to identify voice types in sentences and running speech and the investigation of additional voice types (such as falsetto).