Joint Speech Recognition and Synthesis Training Against Mode Collapse
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Solution Overview
Problem
Unsupervised speech recognition and synthesis models, particularly those based on generative adversarial networks (GANs), suffer from unstable training and mode collapse issues due to the delicate balance between the generator and discriminator, leading to poor output quality and lack of diversity.
Innovation Solution
A joint loss calculation method is applied between unsupervised diffusion-GAN models for speech recognition and synthesis, using a first and second diffusion-GAN model to convert between text and audio, with a combined loss function to improve model accuracy by comparing tensor representations and modifying the models based on the calculated losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If unsupervised GAN models are used for speech recognition and synthesis, then labeled training data is not required, but training stability deteriorates and mode collapse occurs
Solution Approach 1:
The patent merges speech recognition and speech synthesis models into a joint training framework where both models are trained simultaneously with shared components and coordinated optimization. This combination stabilizes training by creating mutual constraints between the two models, preventing mode collapse while eliminating the need for labeled data.
Solution Approach 2:
The patent introduces a shared acoustic model as an intermediary component between the speech recognition and speech synthesis models. This intermediary facilitates stable knowledge transfer and representation learning, enabling unsupervised training while maintaining training stability and preventing generator-discriminator imbalance.
2Quantity of substance
If unsupervised GAN models are used for speech recognition and synthesis, then labeled training data is not required, but output quality deteriorates
Solution Approach 1:
By combining speech recognition and synthesis models in a unified framework with shared acoustic representations, the system achieves high output quality without labeled data. The joint training enables mutual refinement of both models, improving speech recognition accuracy and synthesis quality simultaneously.
Solution Approach 2:
The shared acoustic model serves multiple functions: it provides features for both speech recognition and speech synthesis, enables bidirectional knowledge transfer, and maintains consistent representations across both tasks. This multi-functionality achieves high output quality while eliminating dependency on labeled training data.
3Quantity of substance
If unsupervised GAN models are used for speech recognition and synthesis, then labeled training data is not required, but model diversity deteriorates
Solution Approach 1:
The joint training framework merges speech recognition and synthesis models that process and generate diverse speech data from different speakers, accents, and conditions. This combination preserves and propagates diversity through the shared acoustic model, enabling the system to handle varied inputs and produce diverse outputs without labeled data.
Data Source
AI summary
Disclosed are systems and techniques for training machine learning models. The techniques include providing a first data of a first modality as input to a first machine learning model to obtain a first output of a second modality, providing the first output of the second modality as input to a second machine learning model to obtain a second output of the first modality, providing the first data as input to a third machine learning model to obtain a first tensor, providing the second output as input to the third machine learning model to obtain a second tensor, calculating a first loss based on a comparison between the first tensor and the second tensor, and causing the first machine learning model to be modified based on the first loss.