Recurrent Model Window Padding for Sequence Recognition
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Solution Overview
Problem
Current pattern recognition methods using artificial neural networks face challenges in efficiently processing sequence data, particularly in voice recognition, due to limitations in handling context and padding data, which affects accuracy and training efficiency.
Innovation Solution
A recognition method that extracts target and padding data from sequence data, uses a bidirectional recurrent neural network to calculate recognition results by unfolding the model by window length and padding length, and updates state parameters based on both forward and backward passes, allowing for improved context handling and reduced training errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pattern recognition methods using artificial neural networks are used to process sequence data, then basic recognition functionality is achieved, but accuracy and training efficiency deteriorate due to limitations in handling context and padding data
Solution Approach 1:
The sequence data is divided into fixed-length windows with padding data added to uniformize lengths. This segmentation allows the recurrent model to process variable-length sequences efficiently while maintaining context information, resolving the contradiction between handling diverse input lengths and maintaining training efficiency
Solution Approach 2:
Padding data is prepared in advance and integrated into the training input before model processing. This preliminary action ensures that context information is preserved without requiring complex runtime handling, improving both accuracy and training efficiency
2Measurement precision
If context information is fully utilized in sequence data processing, then recognition accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The sequence is segmented into windows of fixed length with padding, allowing parallel processing of multiple windows. This reduces the computational burden compared to processing the entire sequence sequentially, thereby reducing recognition time while maintaining accuracy through comprehensive context utilization within each window
Solution Approach 2:
The model processes a fixed window length that may include padding data beyond the actual sequence end. This partial action approach processes slightly more data than strictly necessary but enables efficient batch processing and reduces overall recognition time through parallelization
3Productivity
If fixed window length is used for processing sequence data, then processing efficiency improves, but handling of variable-length sequences and context information deteriorates
Solution Approach 1:
Padding data is added in advance to extend variable-length sequences to a uniform fixed window length. This preliminary action preserves the original context information while enabling efficient fixed-window processing, resolving the contradiction between processing efficiency and context preservation
Solution Approach 2:
Padding data acts as an intermediary element that bridges variable-length input sequences and the fixed-window processing requirement. The padding maintains the structural integrity and context flow without interfering with the actual sequence information, allowing efficient processing without context loss
Data Source
AI summary
A recognition method includes extracting target data corresponding to a current window and padding data subsequent to the target data from sequence data; acquiring a state parameter corresponding to a previous window; and calculating a recognition result for the current window based on the state parameter, the extracted target data, and the extracted padding data using a recurrent model.