Papers

Improved Time-Frequency Trajectory Excitation Vocoder for DNN-Based Speech Synthesis

International Conference
2016~2020
작성자
한혜원
작성일
2016-09-01 16:25
조회
276
Authors : Eunwoo Song, Frank K. Soong, Hong-Goo Kang

Year : 2016

Publisher / Conference : INTERSPEECH

We investigate an improved time-frequency trajectory excitation (ITFTE) vocoder for deep neural network (DNN)-based statistical parametric speech synthesis (SPSS) systems. The ITFTE is a linear predictive coding-based vocoder, where a pitch-dependent excitation signal is represented by a periodicity distribution in a time-frequency domain. The proposed method significantly improves the parameterization efficiency of ITFTE vocoder for the DNN-based SPSS system, even if its dimension changes due to the inherent nature of pitch variation. By utilizing an orthogonality property of discrete cosine transform, we not only accurately reconstruct the ITFTE parameters but also improve the perceptual quality of synthesized speech. Objective and subjective test results confirm that the proposed method provides superior synthesized speech compared to the previous system.
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4 International Conference Jin-Seob Kim, Young-Sun Joo, Inseon Jang, ChungHyun Ahn, Jeongil Seo, Hong-Goo Kang "A pitch-synchronous speech analysis and synthesis method for DNN-SPSS system" in 21th International Conference on Digital Signal Processing (DSP), 2016
3 International Conference Eunwoo Song, Frank K. Soong, Hong-Goo Kang "Improved Time-Frequency Trajectory Excitation Vocoder for DNN-Based Speech Synthesis" in INTERSPEECH, 2016
2 International Conference Eunwoo Song, Hong-Goo Kang "Multi-class learning algorithm for deep neural network-based statistical parametric speech synthesis" in EUSIPCO, 2016
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