Effects of Word Embeddings on Neural Network-based Pitch Accent Detection
Table 3: Results (accuracy in %) for within-corpus and cross-corpus pitch accent detection using the baseline acoustic model, with added unigram GloVe word embeddings and 10 bottleneck features, and using only the embeddings as features (shown in italics). The results are averaged across 10 cross-validation splits and 5 repetitions of each experiment.
['TrainTest', 'BURNC', 'BDC', 'LeaP']
As expected, we observe that the performance in the cross-corpus settings drops when compared to the within-corpus setting. BURNC appears to be by far the easiest corpus for pitch accent detection, resulting in accuracy levels obtained while training on BDC or LeaP that are higher than those obtained on the respective within-corpus test sets. The ALL experiments show a slight rise in accuracy that does not quite reach the baseline within-corpus performance, except on LeaP. The BDC datasets appears to be the most difficult to model using this method. Overall, the acoustic model can be said to generalize reasonably well across corpora and genres and