A strategy to solve data scarcity problems in corpus based intonation modelling
Valentín Cardeñoso-Payo, David Escudero-Mancebo · 2004
Data scarcity in corpus-based intonation modelling for text-to-speech (TTS) applications is addressed. Multiple model dictionaries are proposed to predict patterns not found in the training corpus. A grouping strategy is proposed to improve models of classes without a high enough number of training samples. An experimental study of this strategy shows that better pitch profiles can be predicted in this way.