## 4 Classification
### Feature Extraction
The similar jitter distribution across different voice types resulted in a misclassification of all pressed voice samples into modal or breathy voice types. A pilot study was conducted using the same protocol as that of the present study. In the pilot study, participants were 14 female Canadian English speakers aged from 18-40 years with no history of voice disorders. Voice data were recorded using the NSA only. No auditory-perceptual screening was done due to the absence of microphone recordings. Since the sample size (n = 180) of the pilot study was small, the classification of the NSA dataset using machine learning techniques was unsupported. The results of the statistical analysis on various features are shown in Figure 6a,b. Figure 6a shows that the spectral envelopes of the breathy and modal voice of the pilot study had similar contours as those of the present study. The \(H_{2}\) of the pressed voice of the pilot study was less prominent (12%) than that of the present study. In Figure 6b, the SE and shimmer analysis showed similar trends as those in Figure 5b. The modal voice featured the lowest average value and standard deviation for shimmer, and pressed voice featured the highest average value for the SE. For jitter, the breathy voice of the pilot study showed a larger standard deviation (approximately 0.4 more) than that of the present study. No notable difference between jitter in modal and pressed voice was found between the pilot and the present study. Overall, the results from the pilot study were consistent with those of the more exhaustive present study. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Feature** & & **PM** & **PB** & **PP** & **Overall Accuracy** \\ \hline \multirow{2}{*}{\(H_{1}\)} & **TM** & 35\% & 31\% & 34\% & \multirow{2}{*}{64.7\%} \\  & **TB** & 12\% & 84\% & 4\% & \\  & **TP** & 26\% & 6\% & 68\% & \\ \hline \multirow{2}{*}{\(H_{2}\)} & **TM** & 35\% & 37\% & 28\% & \multirow{2}{*}{61.7\%} \\  & **TB** & 12\% & 82\% & 7\% & \\  & **TP** & 29\% & 10\% & 61\% & \\ \hline \multirow{2}{*}{\(H_{3}\)} & **TM** & 24\% & 35\% & 41\% & \multirow{2}{*}{59.3\%} \\  & **TB** & 7\% & 92\% & 2\% & \\  & **TP** & 17\% & 33\% & 50\% & \\ \hline \multirow{2}{*}{\(H_{4}\)} & **TM** & 2\% & 74\% & 24\% & \multirow{2}{*}{44.7\%} \\  & **TB** & 2\% & 87\% & 11\% & \\  & **TP** & 1\% & 71\% & 28\% & \\ \hline \multirow{2}{*}{SE} & **TM** & 0\% & 88\% & 12\% & \multirow{2}{*}{54.1\%} \\  & **TB** & 0\% & 93\% & 7\% & \\  & **TP** & 0\% & 46\% & 54\% & \\ \hline \multirow{2}{*}{\(J_{r}\)} & **TM** & 54\% & 46\% & 0\% & \multirow{2}{*}{47.4\%} \\  & **TB** & 25\% & 75\% & 0\% & \\  & **TP** & 42\% & 58\% & 0\% & \\ \hline \multirow{2}{*}{\(S_{r}\)} & **TM** & 92\% & 2\% & 6\% & \multirow{2}{*}{60.6\%} \\  & **TB** & 18\% & 72\% & 10\% & \\ \cline{1-1}  & **TP** & 56\% & 33\% & 11\% & \\ \hline \end{tabular}
\end{table} 
Table 1: Confusion matrix and overall accuracy in voice type classification using a single feature. TM: True Modal, TB: True Breathy, TP: True Pressed; PM: Predicted Modal, PB: Predicted Breathy, PP: Predicted Pressed. Figure 4: Representative example of normalized NSA waveforms and corresponding spectra of /a:/ in terms of different voice types: modal, breathy and pressed. The frequency resolution is 1 Hz. Figure 5: The averages and standard deviations of \(H_{1}\)–\(H_{4}\), SE, jitter and shimmer based on 31 participants’ pure” NSA samples. The blue, red and green colours represent the modal, pressed and breathy voice types, respectively.