Supplementary materials for JASA - EL manuscript revision

Plot missing phones:

Number of excluded tokens by phone.

Conditional Inference Tree Analyses:

Results of Conditional Inference Trees (CITs) by manner class for VoiceSauce outputs:

This section also includes results for two other measures not presented in the manuscript:

We also present CIT analyses with duration as a factor below for the SoE, CQ and H1-H2, in response to a reviewer’s comment.

plot(ctree(soe ~ manner, data = subset(df, soe != 'NA')), main = 'SOE')

plot(ctree(soe ~ manner + duration, data = subset(df, soe != 'NA')), main = 'SOE (incl. duration)')

plot(ctree(CQ_H ~ manner, data = subset(df, CQ_H != 'NA')), main = 'CQ_H')

plot(ctree(CQ_H ~ manner + duration, data = subset(df, CQ_H != 'NA')), main = 'CQ_H (incl. duration)')

plot(ctree(H1H2u ~ manner, data = subset(df, H1H2u != 'NA')), main = 'H1H2')

plot(ctree(H1H2u ~ manner + duration, data = subset(df, H1H2u != 'NA')), main = 'H1H2 (incl. duration)')

plot(ctree(Energy ~ manner, data = subset(df, Energy != 'NA')), main = 'Energy', cex.main=2.5,cex.lab=2, cex.axis=2)

Note that the glides seem to pattern with more constricted segments here. An examination of F1 in our data suggests that both glides have a very low F1, suggesting that they were indeed produced with a high degree of constriction.

Plotting 2-D spaces for individual segments and manner

Correlation between CQ and SoE:

cor.test(df$CQ_H, df$soe)
## 
##  Pearson's product-moment correlation
## 
## data:  df$CQ_H and df$soe
## t = 12.387, df = 738, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3533830 0.4728205
## sample estimates:
##       cor 
## 0.4148874

Plotting SoE by CQ:

Plotting timecourse within segment

Plotting both CQ_H and SoE on the same plot by speaker, time-aligned, for all segments

Supplementary analyses: only English speakers and English-only coronal segments

This supplementary analysis addresses the concerns a reviewer has raised regarding non-native segments and controlling for possible place of articulation effects.

Supplementary Conditional Inference Tree Analyses

Results of CITs by manner class for all measures presented above: Strength of Excitation (SoE), CQ (Hybrid), H1H2 (uncorrected) and Energy.

plot(ctree(soe ~ manner, data = subset(df, soe != 'NA')), main = 'SOE')

plot(ctree(CQ_H ~ manner, data = subset(df, CQ_H != 'NA')), main = 'CQ_H')

plot(ctree(H1H2u ~ manner, data = subset(df, H1H2u != 'NA')), main = 'H1H2')

plot(ctree(Energy ~ manner, data = subset(df, Energy != 'NA')), main = 'Energy', cex.main=2.5,cex.lab=2, cex.axis=2)