Development of a statistical parametric synthesis system for operatic singing in German

Michael Pucher, Fernando Villavicencio, Junichi Yamagishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper we describe the development of a Hidden Markov Model (HMM) based synthesis system for operatic singing in German, which is an extension of the HMM-based synthesis system for popular songs in Japanese and English called “Sinsy”. The implementation of this system consists of German text analysis, lexicon and Letter-To-Sound (LTS) conversion, and syllable duplication, which enables us to convert a German MusicXML input into context-dependent labels for acoustic modelling.
Using the front-end, we develop two operatic singing voices, female mezzo-soprano and male bass voices, based on our new database, which consists of singing data of professional opera singers based in Vienna. We describe the details of the database and the recording procedure that is used to acquire singing data of four opera singers in German.
For HMM training, we adopt a singer (speaker)-dependent training procedure. For duration modelling we propose a simple method that hierarchically constrains note durations by the overall utterance duration and then constrains phone durations by the synthesised note duration. We evaluate the performance of the voices with two vibrato modelling methods that have been proposed in the literature and show that HM
Original languageEnglish
Title of host publicationProceedings of 9th ISCA Speech Synthesis Workshop
PublisherInternational Speech Communication Association
Number of pages6
Publication statusPublished - 15 Sep 2016
Event9th ISCA Speech Synthesis Workshop (SSW9) - Sunnyvale, United States
Duration: 13 Sep 201615 Sep 2016


Conference9th ISCA Speech Synthesis Workshop (SSW9)
Abbreviated titleSSW9
Country/TerritoryUnited States
Internet address


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