VoiceShop: A Unified Speech-to-Speech Framework for Identity-Preserving Zero-Shot Voice Editing

We present VoiceShop, a novel speech-to-speech framework that can modify multiple attributes of speech, such as age, gender, accent, and speech style, in a single forward pass while preserving the input speaker's timbre. Previous works have been constrained to specialized models that can only edit these attributes individually and suffer from the following pitfalls: the magnitude of the conversion effect is weak, there is no zero-shot capability for out-of-distribution speakers, or the synthesized outputs exhibit undesirable timbre leakage. Our work proposes solutions for each of these issues in a simple modular framework based on a conditional diffusion backbone model with optional normalizing flow-based and sequence-to-sequence speaker attribute-editing modules, whose components can be combined or removed during inference to meet a wide array of tasks without additional model finetuning. Audio samples are available at \url{https://voiceshopai.github.io}.

Non-parallel Accent Conversion using Pseudo Siamese Disentanglement Network

The main goal of accent conversion (AC) is to convert the accent of speech into the target accent while preserving the content and timbre. Previous reference-based methods rely on reference utterances in the inference phase, which limits their practical application. What's more, previous reference-free methods mostly require parallel data in the training phase. In this paper, we propose a reference-free method based on non-parallel data from the perspective of feature disentanglement. Pseudo Siamese Disentanglement Network (PSDN) is proposed to disentangle the accent information from the content representation and model the target accent. Besides, a timbre augmentation method is proposed to enhance the ability of timbre retaining for speakers without target-accent data. Experimental results show that the proposed system can convert the accent of native American English speech into Indian accent with higher accentedness (3.47) than the baseline (2.75) and input (1.19). The naturalness of converted speech is also comparable to that of the input.