## 4 Results  
First, the scores of OS and LC from all listeners were pooled. The OS scores ranged from 1 to 100 (mean \(\pm\) std = 48.0 \(\pm\) 27.0); similarly, the LC scores across speakers ranged from 1 to 100 (mean \(\pm\) std = 43.0 \(\pm\) 27.8). The association between the pooled scores was then evaluated using [PERSON]'s correlation. The two scales had a moderate and positive correlation (r = 0.66, \(p\) < 0.00001). The results of this pooled data analysis are presented in Figure 1.  
Second, the scores of OS and LC were averaged over listeners. The average LC scores across speakers ranged from 8.6 to 86 (mean \(\pm\) std = 43.0 \(\pm\) 21.7); the OS scores ranged from 14.5 to 88.2 (mean \(\pm\) std = 48.0 \(\pm\) 20.3). The association between the average scores was evaluated using [PERSON]'s correlation. The two scales exhibited an almost perfect correlation (r = 0.99, \(p\) < 0.00001). Figure 2 provides the graphic results of this analysis. Based on this statistical analysis, our data would appear to provide evidence that these two perceptual measures are likely to evaluate similar underlying auditory-perceptual characteristics inherent to the TE speaker samples investigated in this study.  
Figure 1: A graphic representation of the association between the perceptual scores of OS and LC when pooled across all listeners. The * symbols represent individual data points and the red line is the line of best fit associated to these data.  
To determine the possible source of differences between the results of Figures 1 and 2, we investigated listener-to-listener variability in the association between OS and LC. To that end, the correlation coefficient between OS and LC over all tokens (TE samples) was computed separately for each listener. Listeners showed a wide range of variability in the association between OS and LC. Five listeners had non-significant correlations, 10 had moderate correlations (r < 0.7), 35 listeners had high correlations (0.7 < r < 0.9), and 1 listener had a very high correlation (r < 0.9 < 1). Figure 3 depicts the histogram of listener-to-listener variabilities in the association between OS and LC. The intra-judge scores may explain some of the individual variability we observed in the correlation between OS and LC.  
In order to further evaluate our data, separate multiple linear regression models were created to explain the average LC scores in terms of spectral and temporal variations of TE speech. The models created based on the spectral and temporal measures of speech accounted for 87.7% and 61.8% of variability in average LC, respectively. The spectral model included the main effect of the inertia of row 11, the sum of row 6, and an interaction effect between the inertia of row 13 and the sum of the last row of the model. All three terms were significant (\(p\) < 0.05). Generally speaking, patients with higher LC scores were more likely to show more limited spectral variations in the portion of the spectrogram that had moderate energy concentration (i.e., the middle rows of the model). The temporal model was based on three predictors, all of which were significant (\(p\) < 0.05). The included predictors were the sum of the last row and the inertia of rows 2 and 12. Generally speaking, patients with higher LC scores were more likely to have very bright regions (i.e., spectro-temporal regions with the highest energy concentration) in their spectrogram. Also, they were more likely to show a wide range of temporal variation in the portion of the spectrogram that had moderate energy concentration (i.e., the middle rows of the model).  
Figure 2: The association between the perceptual scores of OS and LC when averaged across all listeners. The * symbols represent individual data points and the red line is the line of best fit associated to these data.