Disentangled Speech Content and Style Representation
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Solution Overview
Problem
Current technologies face challenges in effectively disentangling linguistic content and speaking style representations from speech data, particularly in unsupervised learning scenarios, which is crucial for applications like speech synthesis, data augmentation, and voice transfer, due to the high dimensionality and complexity of speech waveforms.
Innovation Solution
A linguistic content and speaking style disentanglement model comprising a content encoder, a style encoder, and a decoder is employed, where the content encoder uses vector quantization to generate discrete per-timestep latent representations of linguistic content, and the style encoder utilizes a variational autoencoder to extract global latent style variables, with mutual information loss to minimize correlation between the encoders' outputs, allowing for the separation of content and style information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If speech waveforms are processed as high-dimensional complex data, then comprehensive information is captured, but disentanglement of linguistic content and speaking style becomes difficult
Solution Approach 1:
The patent segments the speech representation into two independent latent spaces: one for linguistic content and another for speaking style. The content encoder processes speech to extract linguistic representations, while the style encoder separately extracts speaking style representations. This segmentation allows each encoder to focus on specific aspects without interference, resolving the complexity of disentanglement while preserving comprehensive information.
Solution Approach 2:
The patent introduces mutual information loss as an intermediary mechanism that mediates between the content encoder and style encoder. By minimizing the mutual information between the two latent representations, the system ensures they remain independent while both capturing relevant information from the speech input, thus simplifying the disentanglement process.
2Measurement precision
If vector quantization is used to generate discrete latent representations, then linguistic content is captured precisely, but speaking style information may be lost
Solution Approach 1:
The patent applies segmentation by separating the representation task into two independent encoders: the content encoder uses vector quantization to precisely capture linguistic content, while the style encoder separately captures speaking style information. This prevents the style information loss that would occur if a single encoder tried to represent both aspects simultaneously.
Solution Approach 2:
The patent merges the outputs of the content encoder and style encoder in the decoder to reconstruct the speech waveform. By combining the discrete linguistic representations from the content encoder with the continuous style representations from the style encoder, the system recovers both precise content information and style information that would otherwise be lost.
3Reliability
If variational autoencoder is used to extract global latent style variables, then speaking style is captured effectively, but computational complexity increases
Solution Approach 1:
The patent applies local quality by using different architectural approaches for different encoders: the content encoder uses vector quantization with discrete representations, while the style encoder uses variational autoencoder with continuous Gaussian distributions. Each encoder is optimized for its specific function, with the style encoder capturing global style variables through mean and variance parameters that require relatively low computational energy compared to full waveform processing.
4Manufacturing precision
If mutual information loss is applied to minimize correlation between encoders, then disentanglement is improved, but training complexity increases
Solution Approach 1:
The patent implements feedback through the mutual information loss term that continuously monitors and penalizes correlation between the content and style latent representations. During training, this feedback signal guides the encoders to produce more independent representations, improving disentanglement precision while the automated optimization process handles the training complexity.
Data Source
AI summary
A linguistic content and speaking style disentanglement model includes a content encoder, a style encoder, and a decoder. The content encoder is configured to receive input speech as input and generate a latent representation of linguistic content for the input speech output. The content encoder is trained to disentangle speaking style information from the latent representation of linguistic content. The style encoder is configured to receive the input speech as input and generate a latent representation of speaking style for the input speech as output. The style encoder is trained to disentangle linguistic content information from the latent representation of speaking style. The decoder is configured to generate output speech based on the latent representation of linguistic content for the input speech and the latent representation of speaking style for the same or different input speech.