## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed with SPSS Statistics (version 25) [49]. Shapiro-Wilk tests were applied to all four dependent variables (VOT, _fo_, CV_fo_, and SPL) to investigate the normality of the data distribution. The results showed a normal distribution for _fo_ (W = 0.99, \(p\) = 0.47), SPL (W = 0.98, \(p\) = 0.32), and VOT (W = 0.99, \(p\) = 0.67) and a non-normal distribution for CV_fo_ (W = 0.84, \(p\) < 0.001). Considering homogeneity of variance as indicated by the Levene statistics (LS), the variables _fo_ (LS = 0.33, \(p\) = 0.56), SPL (LS = 0.44, \(p\) = 0.50), and VOT (LS = 0.07, \(p\) = 0.78) showed homogeneity, while CV_fo_ (LS = 8.0, \(p\) < 0.001) did not. After logarithmic transformation, CV_fo showed normal distribution (W = 0.98, \(p\) = 0.12) and homogeneity of variance (LS = 0.71, \(p\) = 0.40). Thereafter, all dependent variables were suitable for analysis by parametric statistical tests.  
For the first and second aims, to determine the effects of VH condition and VQ on VOT, _fo_, and SPL, a multivariate analysis of variance (MANOVA) was applied. Two interactions were examined: (i) VH (two levels, NPVH and PVH) and (ii) VQ as represented by the OS (three levels, mild, moderate, and severe). Due to the inequality of samples between groups, Pillai's Trace was used to interpret the MANOVA results. The significance level (\(\alpha\)) was set a priori to _p_-values less than 0.05 (_p_ < 0.05). Squared partial curvilinear correlation (\(\text{n}^{2}_{\text{p}}\)) was used to interpret effect sizes [50]. Post hoc testing using the Bonferroni method was run for all combinations of interaction effects.  
Thereafter, [PERSON]'s rank correlation coefficient was applied to explore the relation between VOT, _fo_, SPL, and CV_fo with VQ, because the perceptual voice data (roughness, breathiness, and strain) were non-normally distributed. This analysis considered all three perceptual subcharacteristics of VQ, including roughness, breathiness, and strain, plus the OS. This analysis used numerical values of perceptual judgment (0 to 100) instead of categorical variables (none, mild, moderate, and severe).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Display** & **Setting** \\ \hline Spectrogram & View range (Hz): 0.0 to 5.00 \\ Window length (s): 0.005 & Dynamic Range (dB): 40.0 \\  & View range (Hz): 75 to 500 \\ Pitch setting & Until: Hertz \\  & Analysis method: Cross correlation \\  & Drawing method: Automatic \\  & Default setting \\  & Default setting \\   Window zoom to set voicing boundary & 20 to 50 ms \\  **[p] Acoustic Feature** & **Description**\({}^{*}\) \\   Voiced closure interval (VDCLO) & The voiced portion of the stop closure \\   Voiceless closure interval (VLCLO) & The voiceless portion of the stop closure \\   Release (REL) & Release of the burst \\   Aspiration (ASP) & The aspiration after the burst \\ \hline \hline \end{tabular} Note: * Definitions are described in Kang and Whalen [46].  
\end{table}
Table 3: Description of analysis settings in Praat and acoustic features of [p].  
For the third aim, in order to determine whether the phonetic environment affects VOT, _f_o (mean and CV), and SPL, a further MANOVA analysis was applied, with the following interactions: (i) syllable, i.e., following vowel (four levels, [e; I; o and aj]); (ii) syllable stress (two levels, stress or unstressed); and (iii) mode of speech task (two levels, reading or repetition). Again, gender was included as a co-factor.