Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information
Table 2: Parse and disfluency detection F1 scores on the dev set. Flat-F1 scores were consistently 0.1%-0.3% lower for our models, but 0.2% higher for the Berkeley parser (85.64).
['Model', 'Parse', 'Disf']
First, we note that adding any combination of acoustic-prosodic features (individually or in sets) improves performance over the text-only baseline. However, certain combinations of acoustic-prosodic features are not always better than their subsets. The text + p + δ + f0/E-CNN model that uses all three types of features has the best performance with a gain of 0.7% over the already-strong text-only baseline. We will henceforth refer to the text + p + δ + f0/E-CNN model as our “best model”.