  With the development of speech synthesis, recent research has focused on
challenging tasks, such as speaker generation and emotion intensity control.
Attribute interpolation is a common approach to these tasks. However, most
previous methods for attribute interpolation require specific modules or
training methods. We propose an attribute interpolation method in speech
synthesis by model merging. Model merging is a method that creates new
parameters by only averaging the parameters of base models. The merged model
can generate an output with an intermediate feature of the base models. This
method is easily applicable without specific modules or training methods, as it
uses only existing trained base models. We merged two text-to-speech models to
achieve attribute interpolation and evaluated its performance on speaker
generation and emotion intensity control tasks. As a result, our proposed
method achieved smooth attribute interpolation while keeping the linguistic
content in both tasks.
