Experiment Accuracy (%) WER (%) Enhancement (%) Baseline system 87.79 12.21 ---------- 1 Noun-Adjective 90.18 9.82 2.39 2 Preposition-Word 90.04 9.96 2.25 3 Hybrid (1 & 2) 90.07 9.93 2.28

Table 5 shows that the highest accuracy achieved is in Noun-Adjective case. The reduction in accuracy in the hybrid case is due to the ambiguity introduced in the language model. For more clarification, our method depends on adding new sentences to the transcription corpus that is used to build the language model. Therefore, adding many sentences will finally cause the language model to be biased to some n-grams (1-grams, 2-grams, and 3-grams) on

The common way to evaluate the N-gram language model is using perplexity. The perplexity for the baseline is 34.08. For the proposed cases, the language models' perplexities are displayed in Table 6. The measurements were taken based on the testing set, which contains 9288 words. The enhanced cases are clearly better as their perplexities are lower. The reason for the low perplexities is the specific domains that we used in our
