2024/06/30 by Masato Murata, Murata, Masato, Koichi Miyazaki +3 · 4 citations
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Sound (cs.SD) #Speech Recognition and Synthesis #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2407.00766
openalex publication_date 2024/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.