Subword Segmentation Regularization for OOV Speech Recognition
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Solution Overview
Problem
Modern automated speech recognition (ASR) systems face challenges in handling out-of-vocabulary (OOV) terms and learning relationships between linguistically-related words using word-based representations, while grapheme-based representations require larger memory footprints and computational times, leading to non-optimal performance.
Innovation Solution
Implementing a subword segmentation routine that segments input words into subword units using a trained vocabulary set, applying regularization techniques like misspelling and character swapping to improve ASR model performance, and using architectures like RNN-T or LAS for efficient speech recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Chinese characters are displayed without word segmentation, then the display is simple and continuous, but the meaning cannot be properly conveyed and reading comprehension is impaired
Solution Approach 1:
The patent introduces a word segmentation layer as an intermediary between the Chinese character display layer and the meaning interpretation layer. This segmentation layer divides continuous Chinese characters into meaningful word units using segmentation rules and data structures, enabling accurate meaning conveyance without requiring changes to the fundamental continuous display nature of Chinese characters.
Solution Approach 2:
The patent applies segmentation by dividing the continuous stream of Chinese characters into discrete word units based on linguistic rules and semantic boundaries. Each word unit is represented as a structured data element containing position information, character data, and semantic meaning, allowing the system to process and interpret Chinese text meaningfully while maintaining visual continuity.
2Reliability
If word segmentation is implemented to improve reading comprehension, then meaning conveyance is enhanced, but the processing complexity and computational resources increase
Solution Approach 1:
The patent changes the parameter representation of Chinese text from a simple continuous character stream to a structured array of word units with multiple attributes (position, characters, meaning, length). This parameter transformation enables sophisticated processing and interpretation while providing a standardized framework that manages computational complexity through consistent data structures.
Solution Approach 2:
The patent replaces manual or rule-based word segmentation mechanisms with an automated computing system that uses algorithms and data structures to perform segmentation. This substitution reduces manual intervention complexity while systematically handling the computational tasks required for accurate word boundary detection and meaning assignment.
3Reliability
If traditional word segmentation methods are used, then some meaning can be conveyed, but irregularities and inconsistencies in segmentation results occur
Solution Approach 1:
The patent applies homogeneity by establishing uniform segmentation rules and consistent data structures for all Chinese text processing. The system uses standardized criteria for identifying word boundaries, assigning meanings, and structuring word units, ensuring that the same input always produces the same output format regardless of the specific text content, thereby achieving high segmentation consistency.
Solution Approach 2:
The patent incorporates feedback mechanisms where segmentation results are validated and refined based on linguistic rules and semantic consistency checks. The system adjusts segmentation decisions by evaluating contextual information and applying correction rules, ensuring that irregular cases are resolved consistently and that segmentation accuracy improves through iterative refinement.
Data Source
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AI summary
A method (600) for subword, segmentation includes receiving an input word (302) to be segmented into a plurality of subword units (119). The method also includes executing a subword segmentation routine (300) to segment the input word into a plurality of subword units by accessing a trained vocabulary set (350) of subword units and selecting the plurality of subword units from the input word by greedily finding a longest subword unit from the input word that is present in the trained vocabulary set until an end of the input word is reached.