Multi-language Vector Eigenvector Analysis for Speech Intent
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Solution Overview
Problem
Traditional speech recognition systems rely on lexical similarity, which can lead to inaccuracies when sentences with high word similarity have opposite meanings, particularly in languages like Chinese, causing AI systems to misinterpret user intent.
Innovation Solution
A method that translates user input into multiple languages, creates vector representations, calculates eigenvectors, and assigns scores to determine the semantic meaning, using a multi-language encoder module, attention engine, and intent recognition network to improve intent recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If lexical similarity is used to determine semantic similarity, then processing speed is improved, but accuracy deteriorates when sentences with high word similarity have opposite meanings
Solution Approach 1:
The patent introduces an intermediary multi-language vector space that mediates between lexical form and semantic meaning. By translating sentences into multiple languages and computing eigenvectors in this intermediate space, the system captures semantic relationships that are not apparent from surface lexical similarity alone, thus resolving the contradiction between fast lexical matching and accurate semantic understanding
Solution Approach 2:
The patent moves the semantic analysis from a single-language lexical space to a multi-dimensional eigenvector space derived from multiple language translations. This dimensional transformation allows the system to distinguish between sentences that appear similar lexically but have different semantic meanings, as the eigenvector representation captures nuanced semantic differences across languages
2Measurement precision
If multi-language translation and eigenvector calculation are used to improve semantic accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal multi-language vector space that serves multiple functions: it enables semantic comparison across languages, captures nuanced meaning differences, and provides a standardized framework for intent recognition. This universal representation system handles diverse linguistic inputs through a single unified mechanism, managing complexity by providing a common interface rather than separate processing paths for each language
Data Source
AI summary
A method, system and computer program product for speech recognition using multiple languages includes receiving, by one or more processors, an input from a user, the input includes a sentence in a first language. The one or more processors translate the sentence to a plurality of languages different than the first language, and create vectors associated with the plurality of languages, each vector includes a representation of the sentence in each of the plurality of languages. The one or more processors calculate eigenvectors for each vector associated with a language in the plurality of languages, and based on the calculated eigenvectors, a score is assigned to each of the plurality of languages according to a relevance for determining a meaning of the sentence.