Development of a genre-dependent TTS system with cross-speaker speaking-style transplantation
| Autor: | Lorenzo Trueba, Jaime, Echeverry Correa, Julian David, Barra Chicote, Roberto, San Segundo Hernández, Rubén, Ferreiros López, Javier, Gallardo Antolín, Ascensión, Yamagishi, Junichi, King, Simon, Montero Martínez, Juan Manuel |
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| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: | |
| Zdroj: | 2nd International Workshop on Speech, Language and Audio in Multimedia (SLAM2014) | Proceedings of the 2nd International Workshop on Speech, Language and Audio in Multimedia (SLAM2014) | 11/09/2014-12/09/2014 | Penang, Malaysia Lorenzo-Trueba, J, Echeverry-Correa, J D, Barra-Chicote, R, San-Segundo, R, Ferreiros, J, Gallardo-Antolin, A, Yamagishi, J, King, S & Montero, J M 2014, Development of a Genre-Dependent TTS System with Cross-Speaker Speaking-Style Transplantation . in 2nd International Workshop on Speech, Language and Audio in Multimedia (SLAM2014) . < http://www.isca-speech.org/archive/slam_2014/slm4_039.html > Archivo Digital UPM instname |
| Popis: | One of the biggest challenges in speech synthesis is the production of contextually-appropriate naturally sounding synthetic voices. This means that a Text-To-Speech system must be able to analyze a text beyond the sentence limits in order to select, or even modulate, the speaking style according to a broader context. Our current architecture is based on a two-step approach: text genre identification and speaking style synthesis according to the detected discourse genre. For the final implementation, a set of four genres and their corresponding speaking styles were considered: broadcast news, live sport commentaries, interviews and political speeches. In the final TTS evaluation, the four speaking styles were transplanted to the neutral voices of other speakers not included in the training database. When the transplanted styles were compared to the neutral voices, transplantation was significantly preferred and the similarity to the target speaker was as high as 78%. |
| Databáze: | OpenAIRE |
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