The role of context in neural pitch accent detection in English
Table 2: Test set accuracy of our CNN+LSTM model, compared to the CNN-only baseline of SVS18, a baseline where all content words are labelled as accented, and a baseline where the speech model is given only duration information. The majority baseline performance is 54.4 percent.
['[EMPTY]', '[BOLD] Speech', '[BOLD] Text', '[BOLD] Sp+text']
In contrast, development set This suggests that although text-only context might help identify pitch accented words in principle, even powerful neural models are not well able to exploit the right information (or perhaps require discourse level context, which we did not provide). This conclusion is further supported by an additional analysis where we progressively reduced the vocabulary size of the text-only model from 3000 down to 5. This strongly suggests that either word frequency or the strongly correlated content/function word distinction are the main source of information for the text-only model. Overall, our best speech-only model outperforms the previous work (SVS18) as well as the text-only model and baselines on the test set {exe} \ex{xlist}