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In this paper, we propose a unified training framework for the generation of glottal signals in deep learning (DL)-based parametric speech synthesis systems. The glottal vocoding-based speech synthesis system, especially the modeling-by-generation (MbG) structure that we proposed recently, significantly improves the naturalness of synthesized speech by faithfully representing the noise component of the glottal excitation with an additional DL structure. Because the MbG method introduces adoi:10.21437/interspeech.2018-1590 dblp:conf/interspeech/HwangSKK18 fatcat:vlf7ywjxcvaipndr27ltlmxfyi