One-Shot Voice Conversion Based on Speaker Aware Module

Voice conversion (VC) is a task to convert the voice of speech while preserving its linguistic content. Although several methods have been proposed to enable VC with non-parallel data, it is still difficult to model the voice without a great number of data or an adaptive process. In this paper, we propose a speaker-aware voice conversion (SAVC) system realizing one-shot voice conversion without an adaptation stage. The SAVC utilizes a speaker aware module (SAM) to disentangle speaker embeddings. The SAM comprises a dynamic reference encoder, a static speaker knowledge block (SKB), and a multi-head attention layer. The reference encoder is used to compress a variable-length utterance to a fixed-length vector, the SKB is made up of pre-extraction x-vectors, and the multi-head attention layer is designed to generate weighted combined speaker embeddings. Subsequently, phonetic pos- teriorgrams (PPGs) as context encoding are concatenated with speaker embeddings and sent to the decoder module for generating acoustic features. Experimental results on the Aishell-1 corpus show that the proposed method can improve speaker similarity and converted utterances' speech quality.