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Speech Processing
SPE-P12.5
Poster
Machine Learning for Speech Synthesis II

DISENTANGLING TIMBRE AND SINGING STYLE WITH MULTI-SINGER SINGING SYNTHESIS SYSTEM

Juheon Lee

Date & Time

Thu, May 7, 2020

12:30 pm – 2:30 pm

Location

On-Demand

Abstract

In this study, we define the identity of the singer with two independent concepts - timbre and singing style - and propose a multi-singer singing synthesis system that can model them separately. To this end, we extend our single-singer model into a multi-singer model in the following ways: first, we design a singer identity encoder that can adequately reflect the identity of a singer. Second, we use encoded singer identity to condition the two independent decoders that model timbre and singing style, respectively. Through a user study with the listening tests, we experimentally verify that the proposed framework is capable of generating a natural singing voice of high quality while independently controlling the timbre and singing style. Also, by using the method of changing singing styles while fixing the timbre, we suggest that our proposed network can produce a more expressive singing voice.


Presenter

Juheon Lee

Seoul National University
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Session Chairs

Tomoki Toda

Nagoya University

Zhiyong Wu

Tsinghua University