著者
Yuki SAITO Shinnosuke TAKAMICHI Hiroshi SARUWATARI
出版者
一般社団法人 電子情報通信学会
雑誌
IEICE Transactions on Information and Systems (ISSN:09168532)
巻号頁・発行日
vol.E100.D, no.8, pp.1925-1928, 2017-08-01 (Released:2017-08-01)
参考文献数
20

This paper proposes Deep Neural Network (DNN)-based Voice Conversion (VC) using input-to-output highway networks. VC is a speech synthesis technique that converts input features into output speech parameters, and DNN-based acoustic models for VC are used to estimate the output speech parameters from the input speech parameters. Given that the input and output are often in the same domain (e.g., cepstrum) in VC, this paper proposes a VC using highway networks connected from the input to output. The acoustic models predict the weighted spectral differentials between the input and output spectral parameters. The architecture not only alleviates over-smoothing effects that degrade speech quality, but also effectively represents the characteristics of spectral parameters. The experimental results demonstrate that the proposed architecture outperforms Feed-Forward neural networks in terms of the speech quality and speaker individuality of the converted speech.

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Our new paper on "Voice Conversion Using Input-to-Output Highway Networks" has been published; Open Access https://t.co/HfaN9Zp8yH

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