Style Encoder Adversarial Training for Speech Timbre Accuracy
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Solution Overview
Problem
Conventional text-to-speech (TTS) systems face challenges in achieving high-quality cross-speaker style transfer due to limitations in style embedding vectors, which often contain speaker information, leading to reduced audio fidelity and timbre accuracy in synthesized speech.
Innovation Solution
The proposed solution involves adversarial training of a style encoder using Domain Adversarial Training (DAT) and Variational Auto Encoder (VAE) or Gaussian Mixture Variational Auto Encoder (GMVAE) to generate high-quality style embedding vectors that retain pure style information while removing speaker information, enabling effective cross-speaker style transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional style embedding vectors are used in TTS systems, then the system can generate speech with basic style transfer capability, but the audio fidelity and timbre accuracy deteriorate due to speaker information leakage
Solution Approach 1:
The patent extracts and removes speaker information from the style embedding vector through adversarial training. The speaker identity encoder attempts to predict speaker information from the style embedding, and the gradient reversal layer prevents this prediction by reversing gradients during backpropagation. This effectively extracts harmful speaker information from the style embedding while retaining useful style characteristics, thereby improving timbre accuracy in cross-speaker style transfer.
Solution Approach 2:
The patent introduces a gradient reversal layer as an intermediary component between the style embedding vector and the acoustic model. This intermediary prevents speaker information leakage by reversing the gradient flow during training, allowing the style encoder to learn style representations without encoding speaker identity. This mediator enables reliable style transfer while maintaining high timbre accuracy.
2Adaptability or versatility
If style embedding vectors contain speaker information, then speaker-specific style characteristics can be captured, but cross-speaker style transfer quality deteriorates due to speaker identity interference
Solution Approach 1:
The patent converts the harmful effect of speaker information in style embeddings into a beneficial training mechanism. By introducing a speaker identity encoder and gradient reversal layer, the system uses the presence of speaker information as a teaching signal during adversarial training. The speaker encoder tries to extract speaker identity while the gradient reversal layer prevents successful extraction, thereby teaching the style encoder to represent style without speaker identity. This transforms the problem of speaker information leakage into a solution for achieving speaker-independent style transfer.
3Manufacturing precision
If adversarial training with gradient reversal layer is implemented, then speaker information is removed from style embeddings improving timbre accuracy, but the system complexity increases due to additional components
Solution Approach 1:
The gradient reversal layer serves multiple functions within the system. It not only removes speaker information from style embeddings to improve timbre accuracy, but also enables the style encoder to learn speaker-independent representations, facilitates cross-speaker style transfer, and provides a unified training framework that combines style learning and speaker invariance. This multi-functionality justifies the added complexity by delivering comprehensive improvements across multiple performance dimensions.
Data Source
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AI summary
This disclosure provides methods and apparatuses for training an acoustic model which is for implementing cross-speaker style transfer and comprises at least a style encoder. Training data may be obtained, which comprises a text, a speaker ID, a style ID and acoustic features corresponding to a reference audio. A reference embedding vector may be generated, through the style encoder, based on the acoustic features. Adversarial training may be performed to the reference embedding vector with at least the style ID and the speaker ID, to remove speaker information and retain style information. A style embedding vector may be generated, through the style encoder, based at least on the reference embedding vector being performed the adversarial training. Predicted acoustic features may be generated based at least on a state sequence corresponding to the text, a speaker embedding vector corresponding to the speaker ID, and the style embedding vector.