A Study on Acoustic Parameter Selection Strategies to Improve Deep Learning-Based Speech Synthesis

International Conference
2019-11-01 16:48
Authors : Hyeonjoo Kang, Young-Sun Joo, Inseon Jang, Chunghyun Ahn, Hong-Goo Kang

Year : 2019

Publisher / Conference : APSIPA

In this paper, we investigate the variation in the performance of a deep learning-based speech synthesis (DLSS) system based on the configuration of output acoustic parameters. Our method is mainly applicable for vocoding-based statistical parametric speech synthesis (SPSS), which has advantages in lowresource scenarios. Given the independence assumption of the source-filter model for the spectral and fundamental frequency F0 parameters, we propose a reliable network architecture for training acoustic parameters. Particularly, the F0 parameter suffers from high fluctuation and an extremely low number of dimensions. To relieve these problems, we introduce a contextwindow approach. Furthermore, we apply data augmentation to the proposed structure to overcome a lack of training data, which is a frequent issue with multi-speaker TTS systems. Experimental results confirm the superiority of the proposed algorithm over conventional ones in both single-speaker and multi-speaker TTS setups
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