Shared-Encoder Disentangled Speech Embeddings for Zero-Shot Voice Conversion
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Solution Overview
Problem
Current voice conversion systems require known speaker pairs during training and rely on pre-trained models, failing to generalize to unseen speakers and being sensitive to noise.
Innovation Solution
A disentangled variational autoencoder (VAE) framework with a shared encoder and separate speaker and content encoders, combined with on-the-fly data augmentation, enables zero-shot voice conversion by generating robust speaker and content representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional VC systems use pre-trained speaker models and known speaker pairs during training, then conversion quality is improved, but the system fails to generalize to unseen speakers and becomes sensitive to noise
Solution Approach 1:
The patent segments the speech representation into two independent parts: speaker embedding (identity information) and content embedding (linguistic content). This segmentation allows the system to learn speaker-independent representations during training and then combine them with new speaker embeddings at inference time, enabling generalization to unseen speakers while maintaining conversion quality and robustness.
Solution Approach 2:
The shared encoder learns universal speech representations that are independent of specific speakers. The encoder processes speech segments and produces both speaker and content embeddings that can be applied to any speaker, making the system universally applicable for zero-shot voice conversion without requiring speaker-specific training data.
2Device complexity
If the system uses a shared encoder to encode both speaker and content information, then the model complexity is reduced, but the disentanglement of speaker and content representations becomes more difficult
Solution Approach 1:
The patent extracts speaker information and content information as separate embedding vectors from the shared encoder output. By taking out these two components separately and encoding them through dedicated pathways (speaker encoder and content encoder), the system maintains a simple shared encoder structure while achieving clean disentanglement of speaker and content representations.
Solution Approach 2:
The system transforms the encoder output into a multi-dimensional representation space where speaker embeddings and content embeddings occupy separate dimensions. The speaker embedding captures identity information in one dimension while the content embedding captures linguistic content in another dimension, allowing both to be represented simultaneously without interference.
3Reliability
If the system encodes posterior distributions separately for speaker and content embeddings, then the representation robustness is improved, but the computational cost increases
Solution Approach 1:
The system performs preliminary encoding of speech segments through the shared encoder to obtain both speaker and content embeddings before separately encoding their posterior distributions. This preliminary action extracts the essential information efficiently, and the subsequent separate encoding of posterior distributions operates on already-compressed representations, reducing the overall computational burden while maintaining robustness.
Data Source
AI summary
A method for disentangled variational speech representation learning for voice conversion, performed by at least one processor, is provided. The method includes receiving input speech segments, encoding the input speech segments via a shared encoder to generate a speaker embedding and a content embedding, encoding the posterior distributions of the speaker embedding via a speaker encoder and encoding the posterior distributions of the content embedding via a content encoder to obtain encoded results, and decoding the encoded results by concatenating the encoded results to obtain a reconstructed speech output.