Title :
A strategy to solve data scarcity problems in corpus based intonation modelling
Author :
Cardeñoso, Valentín ; Escudero, David
Author_Institution :
Departamento de Informatica, Valladolid Univ., Spain
Abstract :
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.
Keywords :
dictionaries; linguistics; speech synthesis; statistical analysis; statistical distributions; Bezier functions; acoustic intonation parameters; class grouping; corpus based intonation modelling; data scarcity problems; intonation units; linguistic analysis; linguistic prosodic features; multiple dictionaries; pitch contours; reduced prediction errors; statistical distributions; stress groups; text to speech synthesis; Bibliographies; Classification algorithms; Contracts; Dictionaries; Knowledge based systems; Neural networks; Predictive models; Speech synthesis; Testing; Training data;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on
Print_ISBN :
0-7803-8484-9
DOI :
10.1109/ICASSP.2004.1326073
