Acoustic and Prosodic Discriminators for Zero-Shot Voice Cloning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing text-to-speech (TTS) models struggle to achieve natural and expressive speech in zero-shot voice cloning due to the quality gap between zero-shot and few-shot systems, with neural networks making over-smooth predictions that fail to capture the diversity of human speech.
Innovation Solution
A multi-modal adversarial training framework is employed, utilizing two discriminators - an acoustic discriminator and a prosodic discriminator - to differentiate between synthesized and ground truth features, guiding the training of a TTS generator with an adversarial loss to improve acoustic and prosodic features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional TTS models are used for zero-shot voice cloning, then the system can generate speech without training data, but the speech quality is over-smooth and lacks natural expression
Solution Approach 1:
The speech generation task is segmented into two distinct discrimination tasks: acoustic feature discrimination and prosodic feature discrimination. This segmentation allows the model to separately optimize for different aspects of speech quality, preventing the over-smoothing effect that occurs when trying to optimize all features simultaneously in traditional zero-shot TTS models.
Solution Approach 2:
The patent introduces a new dimension to the training objective by adding prosodic feature discrimination alongside acoustic feature discrimination. This multi-dimensional approach enables the model to capture both the spectral characteristics and the temporal/rhythmic characteristics of speech, thereby improving overall speech quality while maintaining zero-shot capability.
2Stability of the object's composition
If neural networks make smooth predictions for speech synthesis, then the generation process is stable, but the diversity of human speech is not captured
Solution Approach 1:
The patent implements feedback mechanisms through discriminator models that provide gradient signals to the generator. The acoustic discriminator and prosodic discriminator separately evaluate different aspects of generated speech and provide targeted feedback, allowing the generator to maintain stability while capturing speech diversity through adversarial training.
Solution Approach 2:
The patent changes the parameter optimization landscape by introducing separate loss functions for acoustic features and prosodic features. This parameter differentiation allows the model to explore diverse speech patterns without sacrificing generation stability, as each parameter set is optimized independently through its corresponding discriminator.
3Device complexity
If a single discriminator is used for TTS training, then the training process is simple, but both acoustic and prosodic features cannot be effectively differentiated
Solution Approach 1:
The single discriminator is segmented into two specialized discriminators: an acoustic discriminator for spectral features and a prosodic discriminator for temporal/rhythmic features. This segmentation improves measurement precision by allowing each discriminator to specialize in specific feature types, while the overall training framework complexity remains manageable through modular architecture.
Solution Approach 2:
Each discriminator is designed with multi-functionality, handling both classification and feature extraction tasks. The acoustic discriminator processes mel-spectrogram features while the prosodic discriminator processes pitch and energy features, creating a universal framework that can differentiate multiple speech dimensions simultaneously without requiring separate specialized models for each function.
Data Source
AI summary
Systems and methods of multi-modal adversarial training for zero-shot voice cloning are provided. A communication platform provides training transcript data and training speaker data to a text-to-speech (TTS) model to obtains synthesized audio data comprising synthesized acoustic features and synthesized prosodic features. The communication platform determines determine a first classification prediction using a first discriminator model and a second classification prediction using a second discriminator model. The communication platform trains the first discriminator model and the second discriminator model based on the first classification prediction and the second classification prediction. The communication platform trains the TTS model to obtain a trained TTS model based on ground truth acoustic features, ground truth prosodic features, the synthesized acoustic features, the synthesized prosodic features, the first classification prediction, and the second classification prediction.