Multilingual ASR Weight Sharing for Low-Resource Language Adaptation
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Solution Overview
Problem
Existing automatic speech recognition (ASR) models face challenges in multilingual scalability, requiring excessive storage and computational resources due to separate models for each language, and struggle with low-resource languages due to limited training data leading to overfitting and catastrophic forgetting.
Innovation Solution
Training an ASR machine learning model to learn shared weights across multiple languages using a projection layer with a mapping matrix and binary masks, adapting the model to new languages by locking parameters and generating masks for pretrained weights, and tuning with additional languages while preserving learned knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate ASR models are trained for each language, then language-specific accuracy is improved, but storage requirements and computational resources increase significantly
Solution Approach 1:
The patent combines multiple language-specific ASR models into a single multilingual model by sharing acoustic feature extraction layers and language identification layers across all languages, while maintaining separate language-specific projection layers. This merging approach reduces storage requirements from storing multiple complete models to storing one shared model structure with language-specific adaptations.
Solution Approach 2:
The model is segmented into shared components (acoustic feature extraction, language identification) and language-specific components (projection layers). This segmentation allows the system to store common functionality once while maintaining language-specific capabilities through separate projection layers that are activated based on detected language.
2Measurement precision
If separate ASR models are trained for each language, then language-specific accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary training of shared acoustic feature extraction layers and language identification layers on multilingual data before fine-tuning language-specific projection layers. This preliminary action allows the majority of the model to be pre-trained once on diverse languages, significantly reducing the training time required for each individual language compared to training complete separate models.
Solution Approach 2:
Multiple languages are combined into a single training process for the shared model components, allowing simultaneous learning of acoustic features across languages. This combined training approach reduces total training time compared to sequentially training separate models for each language.
3Quantity of substance
If a single multilingual ASR model is used, then storage and training resources are reduced, but performance on low-resource languages deteriorates due to overfitting and catastrophic forgetting
Solution Approach 1:
The model segments language-specific adaptations into separate projection layers that can be independently trained and updated. This segmentation allows low-resource languages to have dedicated projection layers trained on available data without interfering with the shared acoustic features learned from high-resource languages, preventing catastrophic forgetting.
Solution Approach 2:
The patent applies local quality by allowing different parts of the model to have different training characteristics: shared acoustic layers are trained on abundant multilingual data while language-specific projection layers are fine-tuned on language-specific data. This local differentiation enables low-resource languages to receive focused attention in their projection layers without compromising overall model performance.
4Quantity of substance
If a single multilingual ASR model is used, then storage and training resources are reduced, but the model struggles to adapt to new languages
Solution Approach 1:
The patent implements dynamic adaptability by making the projection layers trainable and configurable for new languages. When a new language is added, the system can dynamically create or update the corresponding projection layer with language-specific characteristics while maintaining the shared acoustic backbone, enabling flexible adaptation without retraining the entire model.
Solution Approach 2:
The shared acoustic feature extraction layers and language identification layers serve universal functions across all languages, providing a multi-functional foundation that can support any language. This universality allows the model to adapt to new languages by adding language-specific projection layers while reusing the proven shared components.
Data Source
AI summary
Systems and techniques that facilitate multilingual ASR machine learning models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory that can execute the computer executable components stored in memory. The computer executable components can comprise an automatic speech recognition machine learning model that learns shared weights across one or more languages in a set of target languages; and a training component that trains the automatic speech recognition model.