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Deep neural network context embeddings for model selection in rich-context HMM synthesis., , , , and . INTERSPEECH, page 2207-2211. ISCA, (2015)Analysing Shortcomings of Statistical Parametric Speech Synthesis., , , and . CoRR, (2018)Deep neural network-guided unit selection synthesis., , , , and . ICASSP, page 5145-5149. IEEE, (2016)From HMMS to DNNS: Where do the improvements come from?, , , , and . ICASSP, page 5505-5509. IEEE, (2016)In Other News: a Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited Data., , , , , , , and . NAACL-HLT (2), page 205-213. Association for Computational Linguistics, (2019)Attributing modelling errors in HMM synthesis by stepping gradually from natural to modelled speech., , and . ICASSP, page 4220-4224. IEEE, (2015)Phrase Break Prediction for Long-Form Reading TTS: Exploiting Text Structure Information., , , , , , and . INTERSPEECH, page 1064-1068. ISCA, (2017)Effect of Data Reduction on Sequence-to-sequence Neural TTS., , , , , , and . ICASSP, page 7075-7079. IEEE, (2019)Effect of data reduction on sequence-to-sequence neural TTS., , , , , , and . CoRR, (2018)Overcoming the limitations of statistical parametric speech synthesis.. University of Edinburgh, UK, (2017)British Library, EThOS.