# Semantic Features Based N-Best Rescoring Methods for Automatic Speech Recognition
## 5 Experiments
### Word Embedding Based Rescoring  
We use word embeddings to re-rank the hypotheses list in this section. Figure 6 is similar to Figure 4. In this figure, we show the average sentence word-pair score and word-discourse score at different ranks. Apparently, the sentences ranked at the front score higher than the sentences ranked behind, especially for the sentences ranked at the first 10 places. This shows that the word-pair score and word-discourse score reflect the basic trends of the sentence. Figure 7 shows the probability distribution of 'correct' tags,'substitute' tags and 'insert' tags respectively like Figure 5. For both scores and both testsets, words with 'correct' tags tends to score higher. The average scores of words with the three tags are listed in Table 3.  
The results of the word embedding based word-pair probability score and word-discourse probability score are listed in Table 4. In this experiment, \(\alpha\) is around 0.95, which is the same as LDA based scores. \(k\) is around 1 for word-pair and word-discourse scores without the fallibility score and it is about 0.05 and 0.01 for a word-pair and word-discourse score with fallibility score respectively. Firstly, we can see from the table that features with all dimensions tested in the experiment reduce WER. As for the LDA based features, features with larger dimension do not result in more significant WER reductions. The best results for Dev93 and Eval92 testsets without fallibility weight are both from the 50-dimension GloVe feature, which are 0.20% and 0.44% absolute WER reduction respectively for word-pair score. For the word-discourse score, the 300-dimension GloVe performs well on the whole of the two testsets, which leads to 0.24% and 0.56% absolute WER reductions on Dev93 and Eval92 testsets respectively compared with the 1-best hypotheses. The fallibility weight reduces WER for most features. Similar to the effect on LDA-based scores, fallibility decreases the performance gap among features dimensions for both word-embedding based scores. Figure 6: The average score of word embedding based methods for hypotheses. The horizontal axis, ranging from 1 to 100, represents the rank of hypotheses. The vertical axis represents the average score of hypotheses at the corresponding rank. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Score-Type** & **Feature Dimension** & **Fallibility** & **Dev93 WER** & **Eval92 WER** \\ \hline baseline & - & - & 9.58 & 6.98 \\
2 gram & - & - & 9.57 & 6.88 \\ smallRNN & - & - & 9.44 & 6.57 \\ word-pair & 30 & no & 9.44 & 6.57 \\ word-pair & 30 & yes & 9.33 & 6.45 \\ word-pair & 50 & no & 9.38 & 6.54 \\ word-pair & 50 & yes & 9.30 & 6.50 \\ word-pair & 80 & no & 9.40 & 6.57 \\ word-pair & 80 & yes & 9.33 & 6.52 \\ word-pair & 100 & no & 9.44 & 6.63 \\ word-pair & 100 & yes & 9.34 & 6.54 \\ word-pair & 300 & no & 9.53 & 6.91 \\ word-pair & 300 & yes & 9.39 & 6.68 \\ word-discourse & 30 & no & 9.46 & 6.61 \\ word-discourse & 30 & yes & 9.30 & 6.47 \\ word-discourse & 50 & no & 9.34 & 6.66 \\ word-discourse & 50 & yes & 9.29 & 6.47 \\ word-discourse & 80 & no & 9.39 & 6.56 \\ word-discourse & 80 & yes & 9.30 & 6.52 \\ word-discourse & 100 & no & 9.34 & 6.70 \\ word-discourse & 100 & yes & 9.32 & 6.54 \\ word-discourse & 300 & no & 9.34 & 6.42 \\ word-discourse & 300 & yes & 9.33 & 6.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: WER results of rescoring by word embedding word-pair probability score. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Score-Type** & **Dev93-c** & **Dev93-s** & **Dev93-i** & **Eval92-c** & **Eval92-s** & **Eval92-i** \\ \hline word-pair & \(-9.64\) & \(-10.87\) & \(-11.98\) & \(-9.61\) & \(-11.02\) & \(-12.15\) \\ word-discourse & \(-7.45\) & \(-7.94\) & \(-9.39\) & \(-7.49\) & \(-8.02\) & \(-9.47\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The average scores for words with “c”, “s” and “i” tags for Dev93 and Eval92 testsetsets. Figure 7: Distribution function of word embedding based scores for words with “c”, “s” and “i” tags.