This paper describes a method of modeling singing styles by a statistical method. In this system, singing expression parameters consisting of melody and dynamics which are derived from FO and power are modeled by context-dependent Hidden Markov Models (HMMs.) A modeling method of the parameters are optimized for dealing with them. Since parameters we focus on are essential but general ones for singing synthesizers, generated parameters from the trained models may be possible to be applied to many of them. In the experiment, we trained singing style models by using singing recording with much expressive style, then parameters were generated for songs not included in training data and actually applied to our singing synthesizer VOCALOID. As a result, the style was well perceived in the synthesized sound with good synthetic quality.
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