@conference {371,
	title = {A hierarchical predictor of synthetic speech naturalness using neural networks},
	booktitle = {Proc. Interspeech},
	year = {2016},
	month = {September},
	publisher = {ISCA},
	organization = {ISCA},
	address = {San Francisco, CA},
	abstract = {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.},
	keywords = {Blizzard Challenge, naturalness, neural network, speech synthesis},
	author = {Yoshimura, Takenori and Henter, Gustav Eje and Watts, Oliver and Wester, Mirjam and Yamagishi, Junichi and Tokuda, Keiichi}
}
