Punctuation Prediction Model for Conversational Speech
Table 2: The per-class precision, recall and F1-score (in %) achieved by the CNN and BLSTM models with pre-trained GloVe embeddings. All models used 300-dimensional word embeddings and 1-dimensional boolean conversation side features, and the +T models additionaly used two 1-dimensional time features. The ϵ symbol denotes a blank prediction.
['[BOLD] Model', '[BOLD] Class', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1']
Each model is evaluated with precision, recall and F1 scores for each punctuation class separately. We see that CNN models yield slightly higher precision for the punctuation classes, and BLSTM tends to have the better recall (and the inverse is true for the blank symbol). Although the BLSTM model makes fewer mistakes overall, the punctuation predicted by the CNN model is more accurate - especially in the case of question marks. The word-level time features yield minor improvement in both models, which suggests that the prosodic information carried by the relative word timing and their duration is useful in the punctuation prediction task.