By Topic

Probabilistic principal component analysis applied to voice conversion

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Wilde, M.M. ; Electr. Eng. & Comput. Sci., Tulane Univ., New Orleans, LA, USA ; Martinez, A.B.

In our model for voice conversion, we represent the joint probabilistic acoustic space of the source and target speakers with a mixture of probabilistic principal component analyzers (PPCAs). We present a finer resolution of options to the user of the voice conversion system than traditional Gaussian mixture model based conversion. Objective experiments demonstrate that the dimension of the PPCA directly impacts resulting objective performance but saves both time and memory complexity. Subjective tests imply that incremental removal of information does not affect the listener perceptually. Thus, the end user can select with more freedom how well the system should perform.

Published in:

Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on  (Volume:2 )

Date of Conference:

7-10 Nov. 2004