Asynchronous Optimization for Neural Network Sequence Training
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Solution Overview
Problem
Existing speech recognition technologies face challenges in scaling sequence training due to computationally intensive processing and sequential optimization procedures, making it difficult to handle large datasets effectively.
Innovation Solution
The implementation of asynchronous optimization for training speech models using neural networks, allowing multiple models to be trained in parallel, with each model optimizing its parameters independently and using auxiliary functions to update hidden layers, enabling efficient scalability and randomization of training utterances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequence training is performed using traditional synchronous optimization methods, then model optimization can be achieved, but computational intensity increases and scalability is limited
Solution Approach 1:
The patent divides the training process into independent segments by creating multiple speech model replicas (first speech model, second speech model, etc.), each capable of processing different training utterances simultaneously. This segmentation enables parallel computation while maintaining individual model optimization, directly resolving the contradiction between optimization reliability and training scalability.
Solution Approach 2:
The patent transitions from synchronous single-model training to asynchronous multi-model training by adding the dimension of model replication. Multiple models train independently in parallel across different computational dimensions, transforming a sequential process into a parallel one that scales with computational resources while maintaining optimization quality.
2Productivity
If multiple speech models are trained in parallel asynchronously, then training scalability is improved, but coordination complexity increases
Solution Approach 1:
Each speech model replica independently performs its own training operations using the same training data and optimization algorithms without requiring coordination with other models. The models self-manage their parameter updates and training processes, eliminating the need for complex inter-model coordination while maintaining parallel scalability.
Solution Approach 2:
The patent employs identical training configurations, optimization algorithms, and data processing pipelines across all speech model replicas. This homogeneity ensures that each model follows the same training procedure independently, simplifying the overall system architecture and eliminating coordination complexity while enabling parallel training.
3Reliability
If traditional synchronous optimization is used for sequence training, then parameter updates can be coordinated, but processing time increases
Solution Approach 1:
The patent enables continuous training operations by allowing multiple speech models to process training utterances simultaneously without waiting for each other. Training actions continue in parallel across multiple models, eliminating idle waiting time inherent in synchronous processing and significantly reducing overall training duration while maintaining coordinated parameter updates through the shared training framework.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining, by a first sequence-training speech model, a first batch of training frames that represent speech features of first training utterances; obtaining, by the first sequence-training speech model, one or more first neural network parameters; determining, by the first sequence-training speech model, one or more optimized first neural network parameters based on (i) the first batch of training frames and (ii) the one or more first neural network parameters; obtaining, by a second sequence-training speech model, a second batch of training frames that represent speech features of second training utterances; obtaining one or more second neural network parameters; and determining, by the second sequence-training speech model, one or more optimized second neural network parameters based on (i) the second batch of training frames and (ii) the one or more second neural network parameters.