Non-parallel Voice Conversion based on Hierarchical Latent Embedding Vector Quantized Variational Autoencoder

This paper proposes a hierarchical latent embedding structure for Vector Quantized Variational Autoencoder (VQVAE) to improve the performance of the non-parallel voice conversion (NPVC) model. Previous studies on NPVC based on vanilla VQVAE use a single codebook to encode the linguistic information at a fixed temporal scale. However, the linguistic structure contains different semantic levels (e.g., phoneme, syllable, word) that span at various temporal scales. Therefore, the converted speech may contain unnatural pronunciations which can degrade the naturalness of speech. To tackle this problem, we propose to use the hierarchical latent embedding structure which comprises several vector quantization blocks operating at different temporal scales. When trained with a multi-speaker database, our proposed model can encode the voice characteristics into the speaker embedding vector, which can be used in one-shot learning settings. Results from objective and subjective tests indicate that our proposed model outperforms the conventional VQVAE based model in both intra-lingual and cross-lingual conversion tasks. The official results from Voice Conversion Challenge 2020 reveal that our proposed model achieved the highest naturalness performance among autoencoder based models in both tasks. Our implementation is being made available at https://github.com/tuanvu92/VCC2020