Punctuation Prediction in Spontaneous Conversations: Can We Mitigate ASR Errors with Retrofitted Word Embeddings?
Table 4: Confusion matrix comparison for the CNN-baseline and CNN-100k-mittens models.
['[EMPTY]', 'CNN-100k-mittens  [ITALIC] ϵ', 'CNN-100k-mittens .', 'CNN-100k-mittens ?', 'CNN-100k-mittens ,']
The later model improves the accuracy of all three punctuation classes, with the most pronounced improvement for the ”period” class (9% absolute improvement). The improvement for ”question mark” and ”comma” classes is 6.2% and 6.8%, respectively. The improvements stem mostly from the fact that the CNN-100k-mittens predicts punctuation marks missed by the baseline model. In other words, our model inserts punctuation marks in places where the baseline model predicts blanks.