Paper Info
Title
Objective evaluation of the Dynamic Model Selection method for spectral voice conversion
Abstract
Spectral voice conversion is usually performed using a single model selected in order to represent a tradeoff between goodness of fit and complexity. Recently, we proposed a new method for spectral voice conversion, called Dynamic Model Selection (DMS), in which we assumed that the model topology may change over time, depending on the source acoustic features. In this method a set of models with increasing complexity is considered during the conversion of a source speech signal into a target speech signal. During the conversion, the best model is dynamically selected among the models in the set, according to the acoustical features of each source frame. In this paper, we present an objective evaluation demonstrating that this new method improves the conversion by reducing the transformation error compared to methods based on an single model.
Year
DOI
Venue
2011
10.1109/ICASSP.2011.5947512
Acoustics, Speech and Signal Processing
Keywords
Field
DocType
speech processing,DMS,acoustical features,dynamic model selection,dynamic model selection method,objective evaluation,spectral voice conversion,speech signal processing,Gaussian Mixture Regression,Voice conversion,model selection
Speech processing,Pattern recognition,Computer science,Model selection,Gaussian mixture regression,Artificial intelligence,Hidden Markov model,Goodness of fit
Conference
ISSN
ISBN
Citations 
1520-6149 E-ISBN : 978-1-4577-0537-3
978-1-4577-0537-3
3
PageRank 
References 
Authors
0.40
9
2
Name
Order
Citations
PageRank
Pierre Lanchantin114713.59
Xavier Rodet2627107.87