  Prosody is a rich information source in natural language, serving as a marker
for phenomena such as contrast. In order to make this information available to
downstream tasks, we need a way to detect prosodic events in speech. We propose
a new model for pitch accent detection, inspired by the work of Stehwien et al.
(2018), who presented a CNN-based model for this task. Our model makes greater
use of context by using full utterances as input and adding an LSTM layer. We
find that these innovations lead to an improvement from 87.5% to 88.7% accuracy
on pitch accent detection on American English speech in the Boston University
Radio News Corpus, a state-of-the-art result. We also find that a simple
baseline that just predicts a pitch accent on every content word yields 82.2%
accuracy, and we suggest that this is the appropriate baseline for this task.
Finally, we conduct ablation tests that show pitch is the most important
acoustic feature for this task and this corpus.
