Normal Speech Rate

Figure 5.5 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. Left panel (a) plots the predictions without weighting them by the goodness ratings, while panel (b) on the right plots the predictions after weighting them by the goodness ratings. The x-axis corresponds to the predicted accuracy rates calculated form the L1 labeling task, and the y-axis to the observed

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accuracy rates that were obtained from the L2 labeling task. Plain sounds data are represented by triangle shapes and emphatic sounds data are represented by square shapes.

According to Park and de Jong (2008), segments that happen to fall on the diagonal line in the figure indicate that listeners were relying on their L1 categories to identify those L2 sounds. Based on the figure above, I notice that only Arabic /ðʕ, dʕ, tʕ/ demonstrate a reliance on the L1 categories. The other sounds fall far from the line which means that listeners did not rely on their L1 categories in order to identify these sounds. In addition, as I explained earlier, plain sounds had higher observed accuracy rates than the emphatic sounds. Comparing panels (a) and (b) does not show much difference in terms of the reliance on the L1 categories.

Figure 5.6 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. Similarly, left Figure 5.5. Accuracy Rate Predictions for NG Based On the L1 and L2 Labeling Tasks in NSR 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 5.5. Accuracy rate predictions for NG based on the L1 and L2 labeling tasks in NSR 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 5.5. Accuracy rate predictions for NG based on the L1 and L2 labeling tasks in NSR 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 5.5. Accuracy rate predictions for NG based on the L1 and L2 labeling tasks in NSR 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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panel (a) plots the predictions without weighting them by the goodness ratings, while panel (b) on the right plots the predictions after weighting them by the goodness ratings.

Based on the figure above, I notice that none of the sounds demonstrate a reliance on L1. Only

Arabic /dʕ/ demonstrated some reliance on L1. Also, plain sounds had higher observed accuracy

rates. 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 5.5 with the short vowel condition in Figure 5.6 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 5.6. Accuracy Rate Predictions for NG Based on the L1 and L2 Labeling Tasks in NSR with Short 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 5.6. Accuracy rate predictions for NG based on the L1 and L2 labeling tasks in NSR with short 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 5.6. Accuracy rate predictions for NG based on the L1 and L2 labeling tasks in NSR with short 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 5.6. Accuracy rate predictions for NG based on the L1 and L2 labeling tasks in NSR with short 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 5.7 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 long /a:/ (top row) and shot /a/ (bottom row) were used for the IG and AG. Based on the figure, 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 /ðʕ,

dʕ/ and /tʕ/ in the short vowel condition. 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.

So, by comparing the results from the three listener groups, I notice that there was no

much reliance on the listeners’ L1 in order to identify L2 Arabic sounds. Arabic /ðʕ_{, d}ʕ_{/ were the}

only two sounds that showed reliance on L1. To put this under SLM’s scope, SLM states that the development patterns of “new” L2 sounds are more difficult to predict based on L1 models

because they do not rely on L1 categories. As a result, I can state that Arabic /ðʕ, dʕ/ who showed

reliance on L1 fall under SLM’s “similar” category since they rely on L1. Also, based on the figures, I did not observe much difference between panels (a) and (b). That is an indication that using the goodness ratings with the weighted models did not impact the outcome of the results.

Finally, 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.

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Figure 5.7. Accuracy Rate Predictions for IG and AG Based on the L1 and L2 Labeling Tasks in NSR 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 5.7. Accuracy rate predictions for IG and AG based on the L1 and L2 labeling tasks in NSR 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 5.7. Accuracy rate predictions for IG and AG based on the L1 and L2 labeling tasks in NSR 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 5.7. Accuracy rate predictions for IG and AG based on the L1 and L2 labeling tasks in NSR 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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