Self-Adaptive ASR Distillation for Low-Resource Languages
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Solution Overview
Problem
Automatic speech recognition (ASR) models face performance degradation due to resource disparities across languages, impacting user experience, particularly in low-resource languages where training data is scarce.
Innovation Solution
A method and system for distilling trained teacher ASR models into a multilingual student model using a tunable distillation loss weight, allowing the student model to learn from both teacher and its own training processes, with the weight adjusting based on training steps to balance knowledge transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a multilingual student ASR model is trained using knowledge distillation from teacher models, then the performance and accuracy in low-resource languages is improved, but the model training complexity and computational resources increase
Solution Approach 1:
The patent introduces teacher ASR models as intermediary entities that have been pre-trained on high-resource languages. These teacher models serve as mediators to transfer linguistic knowledge and patterns to the student model, enabling the student model to learn from indirect knowledge rather than requiring extensive direct training data for each low-resource language.
Solution Approach 2:
The patent applies preliminary action by pre-training teacher models on abundant high-resource language data before using them to distill knowledge into the multilingual student model. This preliminary training phase prepares the teacher models with robust linguistic knowledge that can later be transferred to improve performance in low-resource languages without requiring extensive training resources.
2Reliability
If knowledge distillation is applied to transfer learning from high-resource to low-resource languages, then the robustness of ASR models in low-resource scenarios is improved, but the training time and computational cost increase
Solution Approach 1:
The patent employs copying by creating student models that replicate the knowledge and patterns learned by teacher models. Instead of training separate models for each low-resource language from scratch, the system copies knowledge from teacher models trained on high-resource languages, significantly reducing training time while maintaining robustness.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the distillation loss weight as a hyperparameter to control the balance between learning from teacher models and learning from native low-resource language data. By optimizing this parameter, the system achieves robust performance in low-resource scenarios while managing training efficiency.
3Adaptability or versatility
If multiple teacher ASR models are used for distillation into a single student model, then the language coverage and versatility of the student model is improved, but the system complexity and training resources increase
Solution Approach 1:
The patent achieves universality by designing a single multilingual student model that can handle multiple languages through knowledge distillation from multiple teacher models. This unified student model serves multiple functions across different languages, eliminating the need for separate models for each language and reducing overall system complexity despite incorporating knowledge from multiple teachers.
Data Source
AI summary
A method for distilling one or more trained teacher automatic speech recognition (ASR) models into a multilingual student model includes receiving a plurality of teacher training examples and a plurality of student training examples. The method also includes training one or more teacher automatic speech recognition (ASR) models using the plurality of teacher training examples. Each teacher ASR model is configured to output a respective textual representation of a respective audio input. The method further includes generating a multi-lingual student ASR model by training the multi-lingual student ASR model using the plurality of student training examples and distilling the trained one or more teacher ASR models into the multilingual student ASR model using a tunable distillation loss weight. Each student ASR model is configured to receive an audio input and output a corresponding textual representation of the received audio input.