Chi tiết| Tên | Reducing over-smoothness in HMM-based speech synthesis using exemplar-based voice conversion |
| Lĩnh vực | Tin học |
| Tác giả | Gia-Nhu Nguyen, Trung-Nghia Phung |
| Nhà xuất bản / Tạp chí | Năm 2017 |
| Số hiệu ISSN/ISBN | |
| Tóm tắt nội dung | |
Speech synthesis has been applied in many kinds of practical applications. Currently, state-of-the-art speech synthesis
uses statistical methods based on hidden Markov model (HMM). Speech synthesized by statistical methods can be
considered over-smooth caused by the averaging in statistical processing. In the literature, there have been many
studies attempting to solve over-smoothness in speech synthesized by an HMM. However, they are still limited. In this
paper, a hybrid synthesis between HMM and exemplar-based voice conversion has been proposed. The experimental
results show that the proposed method outperforms state-of-the-art HMM synthesis using global variance.
Speech synthesis has been applied in many kinds of practical applications. Currently, state-of-the-art speech synthesisuses statistical methods based on hidden Markov model (HMM). Speech synthesized by statistical methods can beconsidered over-smooth caused by the averaging in statistical processing. In the literature, there have been manystudies attempting to solve over-smoothness in speech synthesized by an HMM. However, they are still limited. In thispaper, a hybrid synthesis between HMM and exemplar-based voice conversion has been proposed. The experimentalresults show that the proposed method outperforms state-of-the-art HMM synthesis using global variance. |
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