Details

Autor(en) / Beteiligte

• Hashimoto, Kei
• Oura, Keiichiro
• Nankaku, Yoshihiko
• Tokuda, Keiichi

Titel

The effect of neural networks in statistical parametric speech synthesis

Ist Teil von

• 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015, p.4455-4459

Ort / Verlag

IEEE

Erscheinungsjahr

2015

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• This paper investigates how to use neural networks in statistical parametric speech synthesis. Recently, deep neural networks (DNNs) have been used for statistical parametric speech synthesis. However, the specific way how DNNs should be used in statistical parametric speech synthesis has not been studied thoroughly. A generation process of statistical parametric speech synthesis based on generative models can be divided into several components, and those components can be represented by DNNs. In this paper, the effect of DNNs for each component is investigated by comparing DNNs with generative models. Experimental results show that the use of a DNN as acoustic models is effective and the parameter generation combined with a DNN improves the naturalness of synthesized speech.