Multi-lingual Speech Recognition with Theme-Semanteme Analysis
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Solution Overview
Problem
Multi-lingual speech recognition systems face challenges in accurately converting human speech into corresponding sentences due to mixed languages and user inaccuracies in pronunciation, leading to semantic meaning discrepancies.
Innovation Solution
A multi-lingual speech recognition and theme-semanteme analysis method that utilizes a processor with a speech recognizer and a semantic analyzer to convert voice input into alphabet strings, determine original words using a pronunciation-alphabet table and multi-lingual vocabulary, and apply correction procedures based on theme vocabulary-semantic relationship data sets to generate corrected sentences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multi-lingual speech recognition is implemented to handle mixed language expressions, then the system's adaptability to different languages is improved, but the accuracy of semantic recognition deteriorates due to pronunciation variations and language mixing
Solution Approach 1:
The patent introduces a theme vocabulary-semantic relationship data set as an intermediary layer between speech recognition and semantic analysis. This mediator contains pre-established relationships between vocabulary items and their semantic meanings, allowing the system to resolve semantic ambiguities caused by multi-lingual mixing and pronunciation variations by referencing this structured knowledge base rather than relying solely on direct speech-to-meaning mapping
Solution Approach 2:
The system performs preliminary processing by extracting theme vocabulary from the recognized speech and matching it against the pre-built theme vocabulary-semantic relationship data set before final semantic determination. This preliminary action allows the system to anticipate and correct potential semantic errors by comparing against established semantic relationships, thereby maintaining high accuracy across multiple languages
2Reliability
If correction procedures are added to improve semantic accuracy, then the reliability of speech recognition is improved, but the device complexity increases
Solution Approach 1:
The correction procedure does not attempt to correct all possible errors in the speech recognition output. Instead, it selectively applies correction only when theme vocabulary is extracted and matched against the semantic relationship data set, performing partial correction on specific critical elements (theme vocabulary) rather than attempting exhaustive correction of the entire sentence, thereby limiting complexity increase while maintaining reliability improvement
Data Source
AI summary
A multi-lingual speech recognition and theme-semanteme analysis method comprises steps executed by a speech recognizer: obtaining an alphabet string corresponding to a voice input signal according to a pronunciation-alphabet table, determining that the alphabet string corresponds to original words according to a multi-lingual vocabulary, and forming a sentence according to the multi-lingual vocabulary and the original words, and comprises steps executed by a sematic analyzer: according to the sentence and a theme vocabulary-semantic relationship data set, selectively executing a correction procedure to generate a corrected sentence, an analysis state determining procedure or a procedure of outputting the sentence, outputting the corrected sentence when the correction procedure successes, and executing the analysis state determining procedure to selectively output a determined result when the correction procedure fails.