We describe results on pitch accent placement in Dutch text obtained with a memory-based learning approach. The training material consists of newspaper texts that have been prosodically annotated by humans, and subsequently enriched with linguistic features and informational metrics using generally available, lowcost, shallow, knowledge-poor tools. We report on the effects of context-modelling and the nearest neighbours parameter (k), and show the advantage of combining features of a different nature, where the best performance yields a cross-validated F-score of 82. Evaluation on an independent test corpus shows that our approach outperforms existing TTS systems for Dutch.
['Erwin Marsi', 'Bertjan Busser', 'Walter Daelemans', 'Veronique Hoste', 'Martin Reynaert', 'Antal van den Bosch']
Combining information sources for memory-based pitch accent placement