4 accepted papers
Recent progress of voice conversion (VC) has achieved a new milestone in speaker cloning and linguistic preservation. But the field remains fragmented, relying on specialized models for linguistic-preserving, expressive, and singing scenarios. We propose OneVoice, a unified zero-shot framework capab
We tend to assume most available speech corpora we use are either completely clean or completely noised. However, the reality is most of them are a mix of both. In this paper, we propose a semi-supervised speech enhancement framework to enhance such typical speech datasets. This framework includes a…
In this paper, a method for estimating the autoregressive parameters from a signal segment is proposed. The method is based on a deep neural network (DNN) in combination with the classical Levinson-Durbin recursion (LDR). The DNN acts as a pre-processor for the LDR and can be trained on different me…
This paper proposes a linear prediction-based part-defined auto-encoder (PAE) network to enhance speech signal. The PAE is a defined decoder or a defined encoder network, based on efficient learning algorithm or classical model. In this paper, the PAE utilizes AR-Wiener filter as decoder part, and t…