Non-Recurrent Neural Network for Automatic Text Punctuation
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Solution Overview
Problem
Automatic speech recognition (ASR) systems produce unstructured text without punctuation or clear boundaries, making it difficult for humans and machines to analyze, as the lack of punctuation and boundaries obscures meaningful portions of the text and causes issues for downstream models.
Innovation Solution
A non-recurrent neural network system is employed to automatically punctuate the text by generating contextualized vectors for each word, which are then processed to predict the likelihood of punctuation marks, allowing for the insertion of appropriate punctuation into the text string.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If ASR systems transcribe speech to text continuously without interruption, then the complete speech content is captured, but the text lacks punctuation and sentence boundaries making it difficult to analyze
Solution Approach 1:
The patent applies segmentation by inserting punctuation marks and sentence boundaries into the continuous ASR transcript. The system divides the unstructured text stream into meaningful sentences and clauses by predicting optimal insertion points for punctuation, thereby maintaining complete speech content while dramatically improving text analyzability and readability.
2Measurement precision
If traditional recurrent neural networks are used for punctuation prediction, then contextual understanding is achieved, but processing time increases due to sequential processing
Solution Approach 1:
The patent replaces the sequential mechanical processing of recurrent neural networks with a parallel processing architecture. By using transformer-based attention mechanisms instead of sequential RNN computations, the system maintains high punctuation prediction accuracy while enabling all tokens to be processed simultaneously, thereby eliminating the sequential processing bottleneck and significantly reducing computation time.
3Reliability
If punctuation is added to every possible word position, then no punctuation is missed, but false punctuation marks increase noise in the text
Solution Approach 1:
The patent employs feedback mechanisms where the model predicts punctuation probabilities for all possible insertion positions, then uses these predictions to inform subsequent processing stages. The system refines its predictions by considering contextual cues and linguistic patterns, selectively confirming or rejecting potential punctuation positions based on accumulated evidence, thereby achieving comprehensive coverage while minimizing false positives.
Data Source
AI summary
A system and method of operating a system for automatically punctuating text using non-recurrent neural networks is disclosed. The system and method at least: applying a text string to a first component of a non-recurrent neural network trained to generate one or more contextualized vectors, wherein the first component determines the contextualized vectors by processing each word in the text string in parallel with one another; applying the contextualized vectors to a second component of the non-recurrent neural network trained to generate a set of probability values for each word in the text string, wherein the second component determines the set of probability values by processing the contextualized vectors in parallel with one another; and transmitting the set of probability values to a text generation engine to generate a formatted text string based on the set of probability values.