A Hierarchical Predictor of Synthetic Speech Naturalness Using Neural Networks

Takenori Yoshimura, Gustav Eje Henter, Oliver Watts, Mirjam Wester, Junichi Yamagishi, Keiichi Tokuda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

A problem when developing and tuning speech synthesis systems is that there is no well-established method of automatically rating the quality of the synthetic speech. This research attempts to obtain a new automated measure which is trained on the result of large-scale subjective evaluations employing many human listeners, i.e., the Blizzard Challenge. To exploit the data, we experiment with linear regression, feed-forward and convolutional neural network models, and combinations of them to regress from synthetic speech to the perceptual scores obtained from listeners. The biggest improvements were seen when combining stimulus- and system-level predictions.
Original languageEnglish
Title of host publicationInterspeech 2016
PublisherInternational Speech Communication Association
Number of pages5
Publication statusPublished - 12 Sept 2016
EventInterspeech 2016 - San Francisco, United States
Duration: 8 Sept 201612 Sept 2016

Publication series

PublisherInternational Speech Communication Association
ISSN (Print)1990-9772


ConferenceInterspeech 2016
Country/TerritoryUnited States
CitySan Francisco
Internet address


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