Zero-Shot Voice Cloning With Incomplete TTS Model Training
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Solution Overview
Problem
Conventional text-to-speech (TTS) systems require large amounts of labeled training data for personalization, making it difficult to generate high-quality synthesized speech for new speakers without additional training.
Innovation Solution
A zero-shot personalized text-to-speech model is developed, comprising a feature extractor, speaker encoder, and text-to-speech module, which extracts acoustic and prosodic features from new target reference speech to generate a personalized voice without requiring new labeled training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional TTS systems use large amounts of labeled training data for personalization, then the quality of synthesized speech for new speakers is improved, but the training computation costs and time increase significantly
Solution Approach 1:
The system performs preliminary extraction of acoustic features and prosodic features from reference speech, and pre-generates speaker embeddings before actual speech synthesis. This allows the TTS model to be quickly adapted to new speakers without requiring extensive retraining, thus reducing training time while maintaining speech quality
Solution Approach 2:
The system extracts and separates key characteristics (acoustic features, prosodic features, speaker embeddings) from reference speech. By extracting only the essential elements needed for voice cloning rather than using complete labeled datasets, the system reduces training requirements while preserving speech quality
2Manufacturing precision
If conventional TTS systems use large amounts of labeled training data for personalization, then the quality of synthesized speech for new speakers is improved, but the training computation costs increase
Solution Approach 1:
The system extracts only the essential features (acoustic features, prosodic features, speaker embeddings) from reference speech rather than processing complete labeled datasets. This extraction approach reduces computational requirements while maintaining the quality of synthesized speech
Solution Approach 2:
The system creates speaker embeddings that capture the essential characteristics of a speaker's voice. These embeddings serve as compact representations that can be used to clone voices without requiring extensive computational resources for full dataset processing
3Measurement precision
If conventional TTS systems require additional training with new labeled training data for new speakers, then the personalization accuracy is improved, but the latency for generating synthesized speech increases
Solution Approach 1:
The system performs preliminary extraction of acoustic features, prosodic features, and speaker embeddings from reference speech before synthesis is needed. This pre-processing allows the model to quickly adapt to new speakers during inference without requiring time-consuming retraining, thus reducing latency while maintaining personalization accuracy
Solution Approach 2:
The system divides the personalization process into separate feature extraction stages (acoustic features, prosodic features, speaker embeddings) that can be independently computed and stored. This segmentation allows these features to be reused during synthesis without reprocessing, reducing latency for new speakers
Data Source
AI summary
Systems and methods are provided for machine learning models configured as zero-shot personalized text-to-speech models which comprise a feature extractor, a speaker encoder, and a text-to-speech module. The feature extractor is configured to extract acoustic features and prosodic features from new target reference speech associated with the new target speaker. The speaker encoder is configured to generate a speaker embedding corresponding to the new target speaker based on the acoustic features extracted from the new target reference speech. The text-to-speech module is configured to generate the personalized voice corresponding for the new target speaker based on the speaker embedding and the prosodic features extracted from the new target reference speech without applying the text-to-speech module on new labeled training data associated with the new target speaker.