ASR Model Knowledge Distillation via Encoder Segmentation
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Solution Overview
Problem
Current federated learning techniques for automatic speech recognition (ASR) models face limitations in efficiently transferring knowledge between global and client models, particularly due to the use of soft labels which may restrict the amount of knowledge transfer and require updating entire models, consuming significant computing resources.
Innovation Solution
Implementing knowledge distillation using principal component analysis (PCA) to compress and transfer knowledge between global and client ASR models, where only the encoder portions are updated, and training client models to predict Bregman coefficients of the global model, reducing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If soft labels are used for knowledge distillation in federated ASR learning, then knowledge can be transferred between global and client models, but the amount of knowledge transfer is restricted and entire models must be updated consuming significant computing resources
Solution Approach 1:
The patent segments the ASR model into encoder and decoder components. Only the encoder portion is updated through knowledge distillation using Bregman coefficients, while the decoder remains frozen. This segmentation allows selective knowledge transfer without requiring updates to the entire model, reducing computational overhead while maintaining effective knowledge transfer from global to client models.
2Loss of information
If entire ASR models are updated during knowledge distillation, then comprehensive knowledge transfer occurs, but computing resources are significantly consumed
Solution Approach 1:
The model is divided into trainable (encoder) and frozen (decoder) segments. Knowledge distillation is applied only to the encoder using Bregman coefficients, capturing essential knowledge from soft labels without requiring full model updates. This selective approach maintains knowledge transfer effectiveness while dramatically reducing computational resource consumption.
Solution Approach 2:
Different parts of the model are treated differently: the encoder is updated with full gradient information from Bregman coefficients to capture local knowledge nuances, while the decoder remains frozen. This local quality differentiation optimizes resource allocation by applying computational effort only where knowledge transfer is most beneficial.
3Productivity
If Bregman coefficients are used for knowledge distillation, then knowledge transfer efficiency improves, but implementation complexity increases
Solution Approach 1:
Bregman coefficients serve as an intermediary representation that bridges the global model's soft labels and the client model's encoder updates. Instead of directly transferring soft labels or updating entire models, the Bregman coefficients mediate the knowledge transfer process, enabling efficient encoder updates while simplifying the overall distillation implementation.
Data Source
AI summary
Information can be distilled from a global automatic speech recognition (ASR) model to a client ASR model. Many implementations include using an RNN-T model as the ASR model, where the global ASR model includes a global encoder, a joint network, a prediction network, and where the client ASR model includes a client encoder, the joint network, and the prediction network. Various implementations include using principal component analysis (PCA) while training the global ASR model to learn a mean vector and a set of principal components corresponding to the global ASR model. Additional or alternative implementations include training the client ASR model to generate one or more predicted coefficients of the global ASR model.