Multi-task Speech Recognition Training Architecture
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Solution Overview
Problem
Existing speech recognition systems face inefficiencies in combining CTC and Seq2Seq models, requiring manual interpolation of loss weights and extensive model training/testing cycles, which is cumbersome and time-consuming.
Innovation Solution
The method automatically balances the importance of CTC and attention loss functions during training, allowing independent optimization of both models, reducing the need for multiple weight settings and streamlining the training process by performing mini-batch alternate training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpolation of loss weights is used to combine CTC and attention models, then recognition accuracy can be optimized, but training time and computational resources increase significantly due to multiple model training cycles
Solution Approach 1:
The system performs self-service by automatically determining the optimal loss weight interpolation parameter through self-supervised learning mechanisms. The model autonomously adjusts the interpolation parameter during training without requiring manual intervention or multiple separate training cycles, thereby reducing training time while maintaining recognition accuracy optimization.
Solution Approach 2:
The interpolation parameter is transformed from a static manual setting to a dynamic variable that automatically adjusts during training. The system dynamically determines the optimal weight interpolation between CTC and attention loss functions based on training progress and performance metrics, eliminating the need for fixed manual weight settings and multiple training cycles.
2Measurement precision
If multiple models with different weights are trained and tested to decide the optimal weight, then recognition accuracy can be improved, but device complexity and operational complexity increase
Solution Approach 1:
The system merges the model selection process into the training process itself. Instead of training and testing multiple separate models with different weights, the invention combines these functions into a single unified training framework that automatically determines the optimal weight interpolation, thereby reducing device complexity and operational complexity.
Solution Approach 2:
The training system achieves multi-functionality by simultaneously performing model training and optimal weight determination within a single framework. This universal approach eliminates the need for separate model building and testing cycles, reducing complexity while maintaining the ability to optimize recognition accuracy across different weight configurations.
3Productivity
If CTC loss and attention loss are combined with fixed interpolation weights, then training efficiency improves, but adaptability to different datasets and tasks decreases
Solution Approach 1:
The system employs dynamic weight interpolation that adapts during training based on dataset characteristics and task requirements. Rather than using fixed weights that compromise adaptability, the interpolation parameter dynamically adjusts to optimize performance for different datasets and tasks while maintaining training efficiency through automated determination mechanisms.
Solution Approach 2:
The invention changes the interpolation parameter from a fixed constant to a variable that automatically adjusts based on training progress and performance feedback. This parameter change enables the system to adapt to different datasets and tasks while maintaining training efficiency, as the optimal parameter is determined through self-supervised mechanisms rather than manual configuration.
Data Source
AI summary
Methods and apparatuses are provided for performing sequence to sequence (Seq2Seq) speech recognition training performed by at least one processor. The method includes acquiring a training set comprising a plurality of pairs of input data and target data corresponding to the input data, encoding the input data into a sequence of hidden states, performing a connectionist temporal classification (CTC) model training based on the sequence of hidden states, performing an attention model training based on the sequence of hidden states, and decoding the sequence of hidden states to generate target labels by independently performing the CTC model training and the attention model training.