Style Transfer Autoencoder for Zero-Shot Voice Conversion
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Solution Overview
Problem
Current voice conversion systems face challenges with non-parallel data, limited ability to perform many-to-many conversions, and the inability to achieve zero-shot voice conversion, where a source speaker's voice is converted to an unseen target speaker's voice based on limited utterances.
Innovation Solution
A style transfer autoencoder system with a specifically designed bottleneck, utilizing a content encoder and a target speaker encoder, allows for zero-shot voice conversion by disentangling speaker style information from source speech and adapting it to the target speaker's style, trained using self-reconstruction loss to ensure distribution matching and effective conversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If voice conversion systems use parallel training data, then conversion accuracy is improved, but data availability and system versatility deteriorate
Solution Approach 1:
The patent segments voice data into two distinct components: speaker-specific style information and content information. The style encoder extracts style features from target speaker data, while the content encoder processes source speech content. This segmentation allows the system to train on non-parallel data by separately learning style representations and content transformations, eliminating the requirement for parallel speech pairs while maintaining conversion accuracy.
Solution Approach 2:
The patent extracts speaker style information as a separate embeddable representation using the style encoder. By taking out the style component from the speech signal and representing it in a compact vector form, the system can apply this extracted style to different content from multiple source speakers. This extraction mechanism enables many-to-many conversion and zero-shot capability without requiring parallel training data between source and target speakers.
2Measurement precision
If voice conversion systems are designed for specific speaker pairs, then conversion quality is improved, but system complexity and adaptability worsen
Solution Approach 1:
The patent creates a universal voice conversion system where a single trained model can perform conversions between any source speaker and any target speaker. The style encoder learns a generalizable representation of speaker style that can be applied to multiple source speakers, and the content encoder is designed to handle various input formats. This multi-functional design eliminates the need for separate models for different speaker pairs, reducing system complexity while maintaining high conversion quality across many-to-many scenarios.
Solution Approach 2:
The patent changes the parameter representation by using continuous style embeddings instead of discrete speaker identifiers. The style encoder outputs continuous vector representations that capture speaker characteristics, allowing smooth interpolation and generalization across speakers. This parameter transformation enables the system to handle unseen target speakers (zero-shot conversion) and multiple source speakers without requiring retraining or complex model architectures.
3Reliability
If voice conversion requires parallel training data, then training reliability is improved, but training flexibility and zero-shot capability worsen
Solution Approach 1:
The patent performs preliminary training of the style encoder on target speaker data before the actual voice conversion task. This preliminary action allows the system to pre-learn robust style representations from the target speaker's speech patterns. By preparing the style encoder in advance with target speaker characteristics, the system achieves reliable training on non-parallel data and gains the flexibility to perform zero-shot conversion to unseen speakers using only their style embeddings.
Solution Approach 2:
The patent introduces style embeddings as an intermediary representation between source content and target output. The style encoder acts as a mediator that transforms target speaker speech into a compact style vector, which then modulates the content from source speakers. This intermediary mechanism enables reliable training on non-parallel data by decoupling the learning of content transformation from style transfer, while providing flexibility for zero-shot conversion through the use of style embeddings from unseen speakers.
Data Source
AI summary
A method (and structure and computer product) to permit zero-shot voice conversion with non-parallel data includes receiving source speaker speech data as input data into a content encoder of a style transfer autoencoder system, the content encoder providing a source speaker disentanglement of the source speaker speech data by reducing speaker style information of the input source speech data while retaining content information and receiving target speaker input speech as input data into a target speaker encoder. The output of the content encoder and the target speaker encoder are combined in a decoder of the style transfer autoencoder, and the output of the decoder provides the content information of the input source speech data in a style of the target speaker speech information.