Fast Speech Rate

Similar to what has been presented above, Figure 5.8 below plots the accuracy in the L2 labeling task for each one of the four target plain and four target emphatic Arabic consonants against calculated predictions of both the unweighted and weighted models when the long vowel /a:/ was used for the NG.

Based on the figure above, I notice that only Arabic emphatic /tʕ/ demonstrates a reliance on L1.

The other L2 sounds, especially the plain sounds, fall far from the diagonal line which means that listeners did not rely on their L1 categories in order to identify these sounds. In addition, as I have explained earlier, plain sounds had higher observed accuracy rates than the emphatic sounds. Also, comparing panels (a) and (b) does not show much difference in terms of the reliance on the L1 categories.

Figure 5.8. Accuracy Rate Predictions for NG Based on the L1 and L2 Labeling Tasks in FSR with Long Vowel /a:/ condition. Left panel (a) Plots Predictions without Weighting by Goodness Ratings, While Right Panel (b) Plots Weighted Predictions. The Line (𝑥 = 𝑦) Indicates an Exact Prediction by the Model.

Figure 5.8. Accuracy rate predictions for NG based on the L1 and L2 labeling tasks in FSR with long vowel /a:/ condition. Left panel (a) plots predictions without weighting by goodness ratings, while right panel (b) plots weighted predictions. The line (𝑥 = 𝑦) indicates an exact prediction by the model.

Figure 5.8. Accuracy rate predictions for NG based on the L1 and L2 labeling tasks in FSR with long vowel /a:/ condition. Left panel (a) plots predictions without weighting by goodness ratings, while right panel (b) plots weighted predictions. The line (𝑥 = 𝑦) indicates an exact prediction by the model.

Figure 5.8. Accuracy rate predictions for NG based on the L1 and L2 labeling tasks in FSR with long vowel /a:/ condition. Left panel (a) plots predictions without weighting by goodness ratings, while right panel (b) plots weighted predictions. The line (𝑥 = 𝑦) indicates an exact prediction by the model.

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Figure 5.9 below plots the accuracy in the L2 labeling task for each one of the four target plain and four target emphatic Arabic consonants against calculated predictions of both the unweighted and weighted models when the short vowel /a/ was used for the NG.

Based on the figure above, I notice that none of the sounds demonstrate a reliance on L1. Comparing panels (a) and (b) does not show any difference in terms of the reliance on the L1 categories. By comparing the long vowel condition in Figure 5.8 with the short vowel condition in Figure 5.9 I notice a slight difference between the two conditions in terms of reliance on L1; more reliance was observed on the long vowel condition. Also, overall observed performance of the listeners in the long vowel condition was better than the short vowel condition for both the plain sounds and emphatic sounds.

Figure 5.9. Accuracy Rate Predictions for NG Based on the L1 and L2 Labeling Tasks in FSR with Short Vowel /a/ Condition. Left Panel (a) Plots Predictions without Weighting by Goodness Ratings, While Right Panel (b) Plots Weighted Predictions.

Figure 5.9. Accuracy rate predictions for NG based on the L1 and L2 labeling tasks in FSR with short vowel /a/ condition. Left panel (a) plots predictions without weighting by goodness ratings, while right panel (b) plots weighted predictions.

Figure 5.9. Accuracy rate predictions for NG based on the L1 and L2 labeling tasks in FSR with short vowel /a/ condition. Left panel (a) plots predictions without weighting by goodness ratings, while right panel (b) plots weighted predictions.

Figure 5.9. Accuracy rate predictions for NG based on the L1 and L2 labeling tasks in FSR with short vowel /a/ condition. Left panel (a) plots predictions without weighting by goodness ratings, while right panel (b) plots weighted predictions.

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Figure 5.10. Accuracy Rate Predictions for IG and AG Based on the L1 and L2 Labeling Tasks in FSR with Long /a:/ (top row) and Short /a/ (bottom row) Condition. Panels (a) Plot Predictions Without Weighting by Goodness Ratings, While Panels (b) Plot Weighted Predictions.

Figure 5.10. Accuracy rate predictions for IG and AG based on the L1 and L2 labeling tasks in FSR with long /a:/ (top row) and short /a/ (bottom row) condition. Panels (a) plot predictions without weighting by goodness ratings, while panels (b) plot weighted predictions.

Figure 5.10. Accuracy rate predictions for IG and AG based on the L1 and L2 labeling tasks in FSR with long /a:/ (top row) and short /a/ (bottom row) condition. Panels (a) plot predictions without weighting by goodness ratings, while panels (b) plot weighted predictions.

Figure 5.10. Accuracy rate predictions for IG and AG based on the L1 and L2 labeling tasks in FSR with long /a:/ (top row) and short /a/ (bottom row) condition. Panels (a) plot predictions without weighting by goodness ratings, while panels (b) plot weighted predictions.

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Figure 5.10. above plots the accuracy in the L2 labeling task for each one of the four target plain and four target emphatic Arabic consonants against calculated predictions of both the unweighted and weighted models when long /a:/ (top row) and shot /a/ (bottom row) were used for the IG and AG. Similar to what was found in the NSR condition, I also notice that there was also not much reliance on L1 by listeners. Listeners of the IG, however, showed some reliance

on L1 for the Arabic sounds /dʕ/ in the long vowel condition, and /tʕ/ in the short vowel condition

in both the unweighted and weighted models. As for the AG, the results show that Arabic

emphatics /ðʕ, dʕ/ showed reliance on L1 in the weighted models, while Arabic /d/ and /sʕ/

showed some reliance on L1 in the unweighted models. The results also did not show much difference between the two vowel conditions. Similar to what has been found earlier, the observed accuracy of the plain sounds was still better than the emphatic sounds. However, performance of the emphatic sounds in the AG was better than the IG which, in turn, was better than the NG.

By comparing the observed accuracies for the three groups, the results in the figures indicate that listeners in all three groups performed better with the plain sounds than the

emphatic sounds. The NG’s performance actually surpassed the performance of the IG and AG for the plain sounds. As for the emphatic sounds, I can see a gradual increase in the performance among the three groups; the NG had the lowest performance followed by the IG and then the AG.

In general, my results did not come out as Park and de Jong’s model expects them to be. It was not very clear to what extent L2 learners are using and facilitating their L1 categories in order to perceive the tested L2 sounds. The results showed that L2 learners performed better than what the model predicts; there was no relationship between the mappings and L2 perceptions.

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However, based on the observed accuracies, the results were successful at showing how L2 exposure affects the overall learnability of L2 emphatic sounds; learners of the AG showed the highest performance rates followed by learners in the IG and then the NG. This is an indication to the overall L2 development that is linked to more L2 exposure. On the other hand, the

observed accuracy results of L2 plain sounds did not demonstrate any performance development.

5.5. The Results in Light of SLM and PAM/PAM-L2