Global Style Token Embeddings for Data-Efficient Speaker Adaptation
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Solution Overview
Problem
Conventional speaker adaptation methods require large amounts of data and fine-tuning of the entire model, which is inefficient and resource-intensive.
Innovation Solution
A speaker adaptation system using a global style token mechanism to generate and predict speaker embeddings, allowing for efficient representation of a speaker's tone without extensive fine-tuning, by constructing a voice conversion model, extracting variance, and predicting a final speaker embedding through similarity comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speaker adaptation methods are used, then speaker tone representation can be achieved, but large amounts of data and extensive fine-tuning are required
Solution Approach 1:
The patent extracts speaker tone characteristics by generating multiple speaker embeddings from a single speaker embedding through the voice conversion model with global style token mechanism. This extraction approach isolates the essential tone representation without requiring large amounts of additional speaker data, directly resolving the contradiction between accurate tone representation and data quantity requirements
Solution Approach 2:
The system performs preliminary generation of multiple speaker embeddings before the adaptation process. By pre-generating a plurality of speaker embeddings that represent different tones of a speaker, the system prepares diverse tone representations in advance, eliminating the need for extensive fine-tuning and large data sets during actual speaker adaptation
2Measurement precision
If conventional speaker adaptation methods are used, then speaker tone representation can be achieved, but the entire model needs to be fine-tuned
Solution Approach 1:
The patent extracts only the necessary speaker embedding representations through the voice conversion model and global style token mechanism, rather than fine-tuning the entire model. This selective extraction approach maintains accurate speaker tone representation while avoiding the complexity of full model fine-tuning
Solution Approach 2:
The system segments the speaker adaptation process into distinct components: generating multiple speaker embeddings using the voice conversion model, selecting appropriate embeddings through similarity comparison, and combining with pitch embeddings. This segmentation allows tone representation without requiring complex full-model fine-tuning
3Adaptability or versatility
If conventional speaker adaptation methods are used, then speaker adaptation can be performed, but resource requirements are high
Solution Approach 1:
The system generates a plurality of speaker embeddings (excessive action) from a single speaker embedding through the voice conversion model. This partial generation approach provides sufficient tone diversity for adaptation without requiring computationally expensive full-model fine-tuning on large data sets, thus reducing overall resource consumption while maintaining adaptability
Data Source
AI summary
Disclosed are a speaker embedding-based speaker adaptation method and system generated by using global style tokens and a prediction model. The speaker adaptation method performed by the speaker adaptation system, according to an embodiment, may comprise the steps of: generating a plurality of speaker embeddings representing the tone of a speaker from a speaker embedding by using a voice transformation model including a global style token mechanism; and predicting the final speaker embedding representing a new speaker through similarity comparison between a new speaker embedding predicted by using a prediction model for predicting a speaker embedding and the plurality of generated speaker embeddings.