Personalized Voice Synthesis through Human-in-the-Loop Coordinate Descent

Abstract

This paper describes a human-in-the-loop approach to personalized voice synthesis in the absence of reference speech data from the target speaker. It is intended to help vocally disabled individuals restore their lost voices without requiring any prior recordings. The proposed approach leverages a learned speaker embedding space. Starting from an initial voice, users iteratively refine the speaker embedding parameters through a coordinate descent-like process, guided by auditory perception. By analyzing the latent space, it is noted that that the embedding parameters correspond to perceptual voice attributes, including pitch, vocal tension, brightness, and nasality, making the search process intuitive. Computer simulations and real-world user studies demonstrate that the proposed approach is effective in approximating target voices across a diverse range of test cases.