ASR Heterograph Resolution via Knowledge Graph Context
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Solution Overview
Problem
Conventional automatic speech recognition (ASR) systems face challenges in accurately processing heterographs, where different words with similar phonetic sounds lead to incorrect outputs or user frustration, as they often randomly select one word or require user clarification.
Innovation Solution
The system encodes verbal input into phoneme sequences and uses a knowledge graph to determine the context of the first word, selecting the word from a heterograph set that is most closely related based on the context and relationships in the graph, thereby resolving ambiguity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ASR systems randomly select one word from heterograph sets, then the system operates quickly and simply, but the output accuracy deteriorates due to incorrect word selection
Solution Approach 1:
The patent introduces an intermediary component (heterograph resolution module with knowledge graph) that mediates between the phoneme recognition stage and the final text output. This intermediary analyzes contextual relationships to disambiguate heterographs, improving accuracy without requiring complete system redesign.
Solution Approach 2:
The system performs preliminary actions by pre-building a knowledge graph that stores contextual relationships between words before ASR processing occurs. During speech recognition, this pre-computed knowledge structure enables rapid disambiguation of heterographs based on contextual clues from previously recognized words.
2Measurement precision
If conventional ASR systems request user clarification for heterographs, then output accuracy improves, but the user experience deteriorates due to frustration and additional input requirements
Solution Approach 1:
The ASR system performs self-service by automatically resolving heterograph ambiguity using contextual analysis and knowledge graph querying. The system independently determines the correct word without requiring user intervention, maintaining high accuracy while preserving ease of operation.
3Measurement precision
If the system uses contextual analysis and knowledge graphs to resolve heterographs, then output accuracy improves, but processing time increases
Solution Approach 1:
The knowledge graph is pre-computed and stored before ASR processing, containing contextual relationships between words. During speech recognition, the system only needs to query this pre-built structure using previously recognized words as keys, rather than performing comprehensive contextual analysis in real-time, thus minimizing processing time overhead.
Solution Approach 2:
The system performs partial contextual analysis by utilizing only the necessary portion of the knowledge graph relevant to the current speech input and previously recognized words. Rather than analyzing all possible contextual relationships, the system focuses on disambiguating specific heterographs using minimal required context, reducing processing time while maintaining accuracy.
Data Source
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AI summary
Systems and methods for performing ASR in the presence of heterographs are provided. Verbal input is received from the user that includes a plurality of utterances. A first of the plurality of utterances is matched to a first word. It is determined that a second utterance in the plurality of utterances matches a plurality of words that is in a same heterograph set. It is identified which one of the plurality of words is associated with a context of the first word. A function is performed based on the first word and the identified one of the plurality of words.