Noise-robust voice conversion with domain adversarial training

TL;DR

This paper introduces a noise-robust voice conversion framework that uses domain adversarial training to extract noise-invariant speaker and content representations, significantly improving speech quality and speaker similarity in noisy conditions.

Contribution

The paper proposes a novel encoder-decoder voice conversion model with domain adversarial training to achieve noise-invariant representations, enhancing robustness in real-world noisy environments.

Findings

Effective in synthesizing clean speech from noisy inputs

Improves speech quality and speaker similarity under noisy conditions

Handles unseen noise types during training

Abstract

Voice conversion has made great progress in the past few years under the studio-quality test scenario in terms of speech quality and speaker similarity. However, in real applications, test speech from source speaker or target speaker can be corrupted by various environment noises, which seriously degrade the speech quality and speaker similarity. In this paper, we propose a novel encoder-decoder based noise-robust voice conversion framework, which consists of a speaker encoder, a content encoder, a decoder, and two domain adversarial neural networks. Specifically, we integrate disentangling speaker and content representation technique with domain adversarial training technique. Domain adversarial training makes speaker representations and content representations extracted by speaker encoder and content encoder from clean speech and noisy speech in the same space, respectively. In this way, the learned speaker and content representations are noise-invariant. Therefore, the two noise-invariant representations can be taken as input by the decoder to predict the clean converted spectrum. The experimental results demonstrate that our proposed method can synthesize clean converted speech under noisy test scenarios, where the source speech and target speech can be corrupted by seen or unseen noise types during the training process. Additionally, both speech quality and speaker similarity are improved.