Papers

Multi-class learning algorithm for deep neural network-based statistical parametric speech synthesis

International Conference
2016~2020
작성자
한혜원
작성일
2016-08-01 16:23
조회
2201
Authors : Eunwoo Song, Hong-Goo Kang

Year : 2016

Publisher / Conference : EUSIPCO

This paper proposes a multi-class learning (MCL) algorithm for a deep neural network (DNN)-based statistical parametric speech synthesis (SPSS) system. Although the DNN-based SPSS system improves the modeling accuracy of statistical parameters, its synthesized speech is often muffled because the training process only considers the global characteristics of the entire set of training data, but does not explicitly consider any local variations. We introduce a DNN-based context clustering algorithm that implicitly divides the training data into several classes, and train them via a shared hidden layer-based MCL algorithm. Since the proposed MCL method efficiently models both the universal and class-dependent characteristics of various phonetic information, it not only avoids the model over-fitting problem but also reduces the over-smoothing effect. Objective and subjective test results also verify that the proposed algorithm performs much better than the conventional method.
전체 365
255 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
254 International Conference Eunwoo Song, Frank K. Soong, Hong-Goo Kang "Improved Time-Frequency Trajectory Excitation Vocoder for DNN-Based Speech Synthesis" in INTERSPEECH, 2016
253 Domestic Conference Hyeonjoo Kang, Young-sun Joo, Wonsuk Jun, Hong-goo Kang "다층신경망 기반 다중 화자 음성변환 시스템" in 한국음향학회 제 33회 음성통신 및 신호처리 학술대회, 2016
252 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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