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Context-adaptive Smoothing for Speech Synthesis  

이기승 (건국대학교 정보통신대학 전자공학과)
김정수 (삼성종합기술원 HCI Lab)
이재원 (삼성종합기술원 HCI Lab)
Abstract
One of the problems that should be solved in Text-To-Speech (TTS) is discontinuities at unit-joining points. To cope with this problem, a smoothing method using a low-pass filter is employed in this paper, In the proposed soothing method, a filter coefficient that controls the amount of smoothing is determined according to contort information to be synthesized. This method efficiently reduces both discontinuities at unit-joining points and artifacts caused by undesired smoothing. The amount of smoothing is determined with discontinuities around unit-joins points in the current synthesized speech and discontinuities predicted from context. The discontinuity predictor is implemented by CART that has context feature variables. To evaluate the performance of the proposed method, a corpus-based concatenative TTS was used as a baseline system. More than 6075 of listeners realized that the quality of the synthesized speech through the proposed smoothing is superior to that of non-smoothing synthesized speech in both naturalness and intelligibility.
Keywords
Text-to-speech synthesis; Waveform concatenation; Smoothing; Context-adaptive filtering; Classification and regression tree;
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