Paper Info
Title
Observation-model error compensation for enhanced spectral envelope transformation in voice conversion
Abstract
A strategy to enhance the signal quality and naturalness was designed for performing probabilistic spectral envelope transformation in voice conversion. The existing modeling error of the probabilistic mixture to represent the observed envelope features is translated generally as an averaging of the information in the spectral domain, resulting in over-smoothed spectra. Moreover, a transformation based on poorly modeled features might not be considered reliable. Our strategy consists of a novel definition of the spectral transformation to compensate the effect of both over-smoothing and poor modeling. The results of an experimental evaluation show that the perceived naturalness of converted speech was enhanced.
Year
DOI
Venue
2015
10.1109/MLSP.2015.7324328
2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)
Keywords
Field
DocType
speech synthesis,voice conversion,voice transformation,linear regression
Errors-in-variables models,Spectral envelope,Pattern recognition,Signal quality,Computer science,Naturalness,Spectral line,Speech recognition,Artificial intelligence,Probabilistic logic,Timbre,Linear predictive coding
Conference
ISSN
Citations 
PageRank 
1551-2541
1
0.34
References 
Authors
13
3
Name
Order
Citations
PageRank
Fernando Villavicencio1656.19
Jordi Bonada223136.11
Yuji Hisaminato392.10