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Authors: Kenji Yasuda ; Ryohei Orihara ; Yuichi Sei ; Yasuyuki Tahara and Akihiko Ohsuga

Affiliation: Graduate School of Information and Engineering, University of Electro-Communications, 1–5–1 Chofugaoka, Chofu-shi, Tokyo, 182–8585, Japan

ISBN: 978-989-758-350-6

Keyword(s): Deep Learning, Domain Transfer, Generative Adversarial Network, Unsupervised Learning, Voice Conversion.

Abstract: In recent years, natural and highly accurate outputs in domain transfer tasks have been achieved by deep learning techniques. Especially, the advent of Generative Adversarial Networks (GANs) has enabled the transfer of objects between unspecified domains. Voice conversion is a popular example of speech domain transfer, which can be paraphrased as domain transfer of speakers. However, most of the voice conversion studies have focused only on transforming the identities of speakers. Understanding other nuances in the voice is necessary for natural speech synthesis. To resolve this issue, we transform the emotions in speech by the most promising GAN model, CycleGAN. In particular, we investigate the usefulness of speech with low emotional intensity as training data. Such speeches are found to be useful when the training data contained multiple speakers.

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Paper citation in several formats:
Yasuda, K.; Orihara, R.; Sei, Y.; Tahara, Y. and Ohsuga, A. (2019). Transforming the Emotion in Speech using a Generative Adversarial Network.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 427-434. DOI: 10.5220/0007258504270434

@conference{icaart19,
author={Kenji Yasuda. and Ryohei Orihara. and Yuichi Sei. and Yasuyuki Tahara. and Akihiko Ohsuga.},
title={Transforming the Emotion in Speech using a Generative Adversarial Network},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={427-434},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007258504270434},
isbn={978-989-758-350-6},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Transforming the Emotion in Speech using a Generative Adversarial Network
SN - 978-989-758-350-6
AU - Yasuda, K.
AU - Orihara, R.
AU - Sei, Y.
AU - Tahara, Y.
AU - Ohsuga, A.
PY - 2019
SP - 427
EP - 434
DO - 10.5220/0007258504270434

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