| Item type |
SIG Technical Reports(1) |
| 公開日 |
2017-02-10 |
| タイトル |
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|
タイトル |
Investigation on an autoregressive recurrent mixture density network for parametric speech synthesis |
| タイトル |
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言語 |
en |
|
タイトル |
Investigation on an autoregressive recurrent mixture density network for parametric speech synthesis |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
|
主題 |
音声合成・応用 |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
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資源タイプ |
technical report |
| 著者所属 |
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National Institute of Informatics |
| 著者所属 |
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National Institute of Informatics |
| 著者所属 |
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National Institute of Informatics |
| 著者所属(英) |
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en |
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National Institute of Informatics |
| 著者所属(英) |
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en |
|
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National Institute of Informatics |
| 著者所属(英) |
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en |
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National Institute of Informatics |
| 著者名 |
Xin, Wang
Shinji, Takaki
Junichi, Yamagishi
|
| 著者名(英) |
Xin, Wang
Shinji, Takaki
Junichi, Yamagishi
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| 論文抄録 |
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内容記述タイプ |
Other |
|
内容記述 |
Neural-network-based mixture density networks are important tools for acoustic modeling in statistical parametric speech synthesis. Recently we found that incorporating an autoregressive model in a recurrent mixture density network, which is referred to as AR-RMDN, enabled the network to generate quite smooth acoustic data trajectories without using the delta and delta-delta coefficients. More interestingly, the new model generated trajectories with a dynamic range similar to that of the natural data, thus alleviating over-smoothing effect. In this work, after explaining the AR-RMDN from the perspective of signal and filter, we compare one AR-RMDN with a modulation-spectrum-based post-filtering method that also eases the over-smoothing effect. It is demonstrated that the AR-RMDN also alters the modulation spectrum of the generated data trajectories but in a different way from the post-filtering method. The AR-RMDN also generates synthetic speech with better perceived quality. Based on the signal and filter interpretation, we further extend the AR-RMDN so that the inverse AR filter can acquire complex poles and stay stable. |
| 論文抄録(英) |
|
|
内容記述タイプ |
Other |
|
内容記述 |
Neural-network-based mixture density networks are important tools for acoustic modeling in statistical parametric speech synthesis. Recently we found that incorporating an autoregressive model in a recurrent mixture density network, which is referred to as AR-RMDN, enabled the network to generate quite smooth acoustic data trajectories without using the delta and delta-delta coefficients. More interestingly, the new model generated trajectories with a dynamic range similar to that of the natural data, thus alleviating over-smoothing effect. In this work, after explaining the AR-RMDN from the perspective of signal and filter, we compare one AR-RMDN with a modulation-spectrum-based post-filtering method that also eases the over-smoothing effect. It is demonstrated that the AR-RMDN also alters the modulation spectrum of the generated data trajectories but in a different way from the post-filtering method. The AR-RMDN also generates synthetic speech with better perceived quality. Based on the signal and filter interpretation, we further extend the AR-RMDN so that the inverse AR filter can acquire complex poles and stay stable. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
|
収録物識別子 |
AN10442647 |
| 書誌情報 |
研究報告音声言語情報処理（SLP）
巻 2017-SLP-115,
号 2,
p. 1-6,
発行日 2017-02-10
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2188-8663 |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-SLP17115002.pdf (974.8 kB)
