One-Shot Voice Conversion by Vector Quantization
Da-Yi Wu, Hung-yi Lee
Abstract
In this paper, we propose a vector quantization (VQ) based one-shot voice conversion (VC) approach without any supervision on speaker label. We model the content embedding as a series of discrete codes and take the difference between quantize-before and quantize-after vector as the speaker embedding. We show that this approach has a strong ability to disentangle the content and speaker information with reconstruction loss only, and one-shot VC is thus achieved.
Topics & Concepts
EmbeddingVector quantizationQuantization (signal processing)Shot (pellet)Speech recognitionComputer scienceOne shotArtificial intelligenceLearning vector quantizationPattern recognition (psychology)AlgorithmEngineeringMechanical engineeringOrganic chemistryChemistrySpeech Recognition and SynthesisSpeech and Audio ProcessingAdvanced Data Compression Techniques