Multiplicative Integration in Neural Network Transducer Models
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Solution Overview
Problem
Current joint network implementations in end-to-end speech recognition models fail to adequately model interactions between acoustic and language models, lacking sufficient incorporation of additive terms and second-order interactions, which results in higher error rates and inadequate control over network changes during training.
Innovation Solution
The proposed solution involves a multiplicative integration method in neural network transducer models, where the joint vector is computed by element-wise multiplication of the encoder and prediction vectors, combined with additive terms, and controlled through linear layers and bias vectors, to improve the interaction modeling and reduce error rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additive integration is used in the joint network to combine encoder and prediction vectors, then the model structure is simple and training is easy, but the interaction modeling between acoustic and language models is insufficient leading to higher error rates
Solution Approach 1:
The patent combines multiple integration methods (additive and multiplicative) into a composite integration mechanism. The joint network uses both additive terms (encoder vector + prediction vector) and multiplicative terms (encoder vector ⊗ prediction vector) to create a composite representation that captures both linear and interaction effects, thereby reducing error rates while maintaining manageable complexity through structured combination of simple operations.
2Device complexity
If multiplicative integration is applied to model second-order interactions, then the interaction modeling is enhanced, but the control over network changes during training becomes more difficult
Solution Approach 1:
The patent introduces dynamic gating mechanisms that learn to modulate the multiplicative interactions during training. The gate vectors are updated via backpropagation to optimally control the strength of multiplicative terms, enabling the network to dynamically adjust interaction modeling complexity based on training progress and data characteristics, thus maintaining ease of operation while enhancing interaction capabilities.
3Productivity
If present joint network implementations are used with simple additive combinations, then the training is straightforward and fast, but the accuracy of speech recognition is reduced due to insufficient interaction modeling
Solution Approach 1:
The patent segments the integration process into distinct additive and multiplicative components that can be computed separately and then combined. This segmentation allows for efficient computation where the additive part provides fast baseline integration and the multiplicative part adds interaction modeling, maintaining training speed while improving accuracy through modular computation of different integration modes.
Data Source
AI summary
Using an encoder neural network model, an encoder vector is computed, the encoder vector comprising a vector representation of a current portion of input data in an input sequence. Using a prediction neural network model, a prediction vector is predicted, the prediction performed using a previous prediction vector and a previous output symbol corresponding to a previous portion of input data in the input sequence. Using a joint neural network model, a joint vector corresponding to the encoder vector and the prediction vector is computed, the joint vector multiplicatively combining each element of the encoder vector with a corresponding element of the prediction vector. Using a softmax function, the joint vector is converted to a probability distribution comprising a probability that a current output symbol corresponds to the current portion of input data in the input sequence.