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

Eunwoo Song, Hong-Goo Kang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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 layerbased 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.

Original languageEnglish
Title of host publication2016 24th European Signal Processing Conference, EUSIPCO 2016
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1951-1955
Number of pages5
Volume2016-November
ISBN (Electronic)9780992862657
DOIs
Publication statusPublished - 2016 Nov 28
Event24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary
Duration: 2016 Aug 282016 Sep 2

Other

Other24th European Signal Processing Conference, EUSIPCO 2016
CountryHungary
CityBudapest
Period16/8/2816/9/2

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All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Song, E., & Kang, H-G. (2016). Multi-class learning algorithm for deep neural network-based statistical parametric speech synthesis. In 2016 24th European Signal Processing Conference, EUSIPCO 2016 (Vol. 2016-November, pp. 1951-1955). [7760589] European Signal Processing Conference, EUSIPCO. https://doi.org/10.1109/EUSIPCO.2016.7760589