Generalizable Zero-Shot Speaker Adaptive Speech Synthesis with Disentangled Representations

TL;DR

This paper introduces GZS-TV, a novel zero-shot speaker adaptive speech synthesis model that uses disentangled representations to improve generalization and synthesis quality for unseen speakers.

Contribution

The paper presents GZS-TV, a new model employing disentangled representation learning and variational autoencoders to enhance zero-shot speaker adaptation in speech synthesis.

Findings

GZS-TV reduces performance degradation on unseen speakers.

GZS-TV outperforms baseline models across multiple datasets.

Disentangled representations improve model generalization.

Abstract

While most research into speech synthesis has focused on synthesizing high-quality speech for in-dataset speakers, an equally essential yet unsolved problem is synthesizing speech for unseen speakers who are out-of-dataset with limited reference data, i.e., speaker adaptive speech synthesis. Many studies have proposed zero-shot speaker adaptive text-to-speech and voice conversion approaches aimed at this task. However, most current approaches suffer from the degradation of naturalness and speaker similarity when synthesizing speech for unseen speakers (i.e., speakers not in the training dataset) due to the poor generalizability of the model in out-of-distribution data. To address this problem, we propose GZS-TV, a generalizable zero-shot speaker adaptive text-to-speech and voice conversion model. GZS-TV introduces disentangled representation learning for both speaker embedding extraction and timbre transformation to improve model generalization and leverages the representation learning capability of the variational autoencoder to enhance the speaker encoder. Our experiments demonstrate that GZS-TV reduces performance degradation on unseen speakers and outperforms all baseline models in multiple datasets.