Word Clustering for Speech Recognition Domain Ambiguity
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Solution Overview
Problem
In speech recognition systems, the ambiguity in domain classification due to varying domain associations with the same word across different developers leads to increased computational complexity and accuracy issues, as the same word may belong to different domains, making it difficult to clearly define and classify the correlation between domains and words.
Innovation Solution
The method involves clustering words into groups based on domain information using learning data and mass text data, generating embedding vectors to combine words with similar meanings into final clustering groups, employing models like Bag-of-Words, Word2Vec, or One-hot models to refine the word database and improve domain classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the same word is stored in multiple domains by different developers, then the word database becomes more comprehensive, but domain classification accuracy deteriorates due to ambiguity
Solution Approach 1:
The patent segments words into different clustering groups based on their domain associations. Words that appear in multiple domains are divided into separate clustering groups for each domain, allowing the system to maintain comprehensive word coverage while resolving classification ambiguity through structured segmentation
Solution Approach 2:
The patent introduces clustering groups as an intermediary layer between words and domains. This intermediary structure resolves the ambiguity by organizing words into domain-specific clusters, enabling accurate domain classification while preserving the comprehensive nature of the word database
2Adaptability or versatility
If multi-hot feature extraction is used to handle words in multiple domains, then word representation becomes more flexible, but computational complexity increases
Solution Approach 1:
The patent segments the computational process into two stages: offline clustering to generate domain-specific word groups, and online classification to select appropriate clusters. This segmentation reduces online computational complexity while maintaining flexible word representation through pre-organized clustering structures
Solution Approach 2:
The patent performs word clustering as a preliminary offline action, organizing words into domain-specific groups before actual classification tasks. This preliminary organization reduces the computational burden during real-time operations while preserving representation flexibility
Data Source
AI summary
A control method of an electronic apparatus includes clustering words included in a plurality of domains respectively into a plurality of clustering groups based on information corresponding to the plurality of domains; obtaining an embedding vector between the words and the plurality of clustering groups based on learning data corresponding to respective domains, the words, and mass text learning data; and generating one or more final clustering groups of the words based on the obtained embedding vector and the plurality of clustering group.