By Topic

Evaluation of Expressive Speech Synthesis With Voice Conversion and Copy Resynthesis Techniques

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)
Turk, O. ; Sensory, Inc., Portland, OR, USA ; Schroder, M.

Generating expressive synthetic voices requires carefully designed databases that contain sufficient amount of expressive speech material. This paper investigates voice conversion and modification techniques to reduce database collection and processing efforts while maintaining acceptable quality and naturalness. In a factorial design, we study the relative contributions of voice quality and prosody as well as the amount of distortions introduced by the respective signal manipulation steps. The unit selection engine in our open source and modular text-to-speech (TTS) framework MARY is extended with voice quality transformation using either GMM-based prediction or vocal tract copy resynthesis. These algorithms are then cross-combined with various prosody copy resynthesis methods. The overall expressive speech generation process functions as a postprocessing step on TTS outputs to transform neutral synthetic speech into aggressive, cheerful, or depressed speech. Cross-combinations of voice quality and prosody transformation algorithms are compared in listening tests for perceived expressive style and quality. The results show that there is a tradeoff between identification and naturalness. Combined modeling of both voice quality and prosody leads to the best identification scores at the expense of lowest naturalness ratings. The fine detail of both voice quality and prosody, as preserved by the copy synthesis, did contribute to a better identification as compared to the approximate models.

Published in:

Audio, Speech, and Language Processing, IEEE Transactions on  (Volume:18 ,  Issue: 5 )