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Speaker specific phrase break modeling with conditional random fields for text-to-speech
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| dc.contributor.author |
Louw, Johannes A
|
|
| dc.contributor.author |
Moodley, Avashlin
|
|
| dc.date.accessioned | 2019-04-12T08:51:53Z | |
| dc.date.available | 2019-04-12T08:51:53Z | |
| dc.date.issued | 2016-12 | |
| dc.identifier.citation | Louw, J.A. & Moodley, A. 2016. Speaker specific phrase break modeling with conditional random fields for text-to-speech. In: 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference, 30 November - 2 December 2016, Stellenbosch, South Africa | en_US |
| dc.identifier.issn | 978-1-5090-3334 | |
| dc.identifier.uri | https://ieeexplore.ieee.org/document/7813163 | |
| dc.identifier.uri | DOI: 10.1109/RoboMech.2016.7813163 | |
| dc.identifier.uri | http://hdl.handle.net/10204/10966 | |
| dc.description | Presented in: 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference, 30 November - 2 December 2016, Stellenbosch, South Africa. Due to copyright restrictions, the attached PDF file only contains the abstract of the full-text item. For access to the full-text item, please consult the publisher's website. While waiting for the post-print or published PDF document from the publisher | en_US |
| dc.description.abstract | In this paper we present a new cascading conditional random field based phrase break model for text-to-speech systems, trained on the speaker specific acoustic data that the text-to-speech voices are trained on. The training phase does not require any manually labeled phrase break tags, as these are derived directly from the speaker specific recordings used for building the synthetic voices. We present objective evaluations on various corpora, and show that the proposed model compares well with state-of-the-art data-driven phrase break models, with the added benefit of being in a unified framework. | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartofseries | Workflow;18124 | |
| dc.subject | Text-to-speech systems | en_US |
| dc.subject | Phrase breaks | en_US |
| dc.subject | Prosodic phrasing | en_US |
| dc.title | Speaker specific phrase break modeling with conditional random fields for text-to-speech | en_US |
| dc.type | Conference Presentation | en_US |
| dc.identifier.apacitation | Louw, J. A., & Moodley, A. (2016). Speaker specific phrase break modeling with conditional random fields for text-to-speech. http://hdl.handle.net/10204/10966 | en_ZA |
| dc.identifier.chicagocitation | Louw, Johannes A, and Avashlin Moodley. "Speaker specific phrase break modeling with conditional random fields for text-to-speech." (2016): http://hdl.handle.net/10204/10966 | en_ZA |
| dc.identifier.vancouvercitation | Louw JA, Moodley A, Speaker specific phrase break modeling with conditional random fields for text-to-speech; 2016. http://hdl.handle.net/10204/10966 . | en_ZA |
| dc.identifier.ris | TY - Conference Presentation AU - Louw, Johannes A AU - Moodley, Avashlin AB - In this paper we present a new cascading conditional random field based phrase break model for text-to-speech systems, trained on the speaker specific acoustic data that the text-to-speech voices are trained on. The training phase does not require any manually labeled phrase break tags, as these are derived directly from the speaker specific recordings used for building the synthetic voices. We present objective evaluations on various corpora, and show that the proposed model compares well with state-of-the-art data-driven phrase break models, with the added benefit of being in a unified framework. DA - 2016-12 DB - ResearchSpace DP - CSIR KW - Text-to-speech systems KW - Phrase breaks KW - Prosodic phrasing LK - https://researchspace.csir.co.za PY - 2016 SM - 978-1-5090-3334 T1 - Speaker specific phrase break modeling with conditional random fields for text-to-speech TI - Speaker specific phrase break modeling with conditional random fields for text-to-speech UR - http://hdl.handle.net/10204/10966 ER - | en_ZA |
