# An Attribute Interpolation Method in Speech Synthesis by Model Merging

arXiv: https://arxiv.org/abs/2407.00766 · HTML: https://mlsanity.com/paper/2407.00766/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2024.

## Built on by (most cited first, 4 of 4)

- [Task Arithmetic for Language Expansion in Speech Translation](https://mlsanity.com/paper/2409.11274/) (2024)
- [Speech-FT: Merging Pre-trained And Fine-Tuned Speech Representation Models For Cross-Task Generalization](https://mlsanity.com/paper/2502.12672/) (2025)
- [A correlation-permutation approach for speech-music encoders model merging](https://mlsanity.com/paper/2506.11403/) (2025)
- [Exploring the potential and limitations of Model Merging for Multi-Domain Adaptation in ASR](https://mlsanity.com/paper/2603.05354/) (2026)

## Abstract (arXiv)

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.