Dynamic Convolution Text Labeling for Punctuation Prediction
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Solution Overview
Problem
Existing methods for punctuation prediction in speech recognition, such as those using recurrent neural networks and self-attention mechanisms, suffer from low efficiency and accuracy due to their dependency on long sequences, leading to ambiguous and inaccurate translation results.
Innovation Solution
A text labeling method that processes input information to obtain word embedding representation, performs dynamic convolution feature extraction, and inserts labels based on classification results, specifically using a time window context for feature extraction to determine punctuation accurately and efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If recurrent neural network is used for feature extraction, then punctuation prediction can be performed, but the speed of feature extraction is slow and speech input may be forgotten due to time sequence dependency
Solution Approach 1:
The patent replaces the mechanical sequential processing of RNN with parallel convolutional processing. The convolutional neural network processes all time steps simultaneously through convolution operations, eliminating the sequential dependency bottleneck and achieving both fast extraction and accurate punctuation prediction.
Solution Approach 2:
The patent introduces spatial convolution operations that work across the time dimension parallelly. By applying convolution kernels at different positions simultaneously, the system extracts features from the entire speech sequence in one operation rather than processing sequentially, thus increasing productivity while maintaining reliability.
2Productivity
If self-attention mechanism is used for feature extraction, then time sequence dependency is eliminated, but efficiency is low when input text is very long due to weighting all context information
Solution Approach 1:
The patent segments the context information into local windows using convolutional kernels with finite receptive fields. Instead of processing the entire long sequence at once, the convolutional network processes local segments in parallel, reducing the effective context window size and improving efficiency for long inputs while maintaining accurate punctuation prediction.
3Reliability
If self-attention mechanism weights all context information, then feature extraction can be performed, but accuracy of punctuation determination is affected when there is a large amount of context information
Solution Approach 1:
The patent applies local quality by using convolutional kernels with specific receptive field sizes that focus on local context windows around each position. This local focus allows the model to capture punctuation patterns in the immediate context without being diluted by distant irrelevant information, thereby improving accuracy while reducing processing time for long sequences.
Data Source
AI summary
Aspects of the present disclosure relate to a text labeling method and device, and more specifically to the field of deep learning and solving the problem of low efficiency and low accuracy of a feature extraction process. The method can include processing input information to obtain word embedding representation information of the input information, performing dynamic convolution feature extraction on the word embedding representation information to obtain a classification result of each character from the word embedding representation information, and inserting a label into the input information based on the classification result.