@conference {henter2016robust,
	title = {Robust {TTS} duration modelling using {DNN}s},
	booktitle = {Proc. ICASSP},
	volume = {41},
	year = {2016},
	month = {March},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Shanghai, China},
	abstract = {Accurate modelling and prediction of speech-sound durations is an important component in generating more natural synthetic speech. Deep neural networks (DNNs) offer a powerful modelling paradigm, and large, found corpora of natural and expressive speech are easy to acquire for training them. Unfortunately, found datasets are seldom subject to the quality-control that traditional synthesis methods expect. Common issues likely to affect duration modelling include transcription errors, reductions, filled pauses, and forced-alignment inaccuracies. To combat this, we propose to improve modelling and prediction of speech durations using methods from robust statistics, which are able to disregard ill-fitting points in the training material. We describe a robust fitting criterion based on the density power divergence (the beta-divergence) and a robust generation heuristic using mixture density networks (MDNs). Perceptual tests indicate that subjects prefer synthetic speech generated using robust models of duration over the baselines.},
	keywords = {duration modelling, robust statistics, speech synthesis},
	url = {http://homepages.inf.ed.ac.uk/ghenter/pubs/henter2016robust.pdf},
	author = {Henter, Gustav Eje and Ronanki, Srikanth and Watts, Oliver and Wester, Mirjam and Wu, Zhizheng and King, Simon}
}
