Multilingual Codebook Generation for Embedded Speech Recognition
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Solution Overview
Problem
Current speech recognition systems face challenges in efficiently recognizing speech inputs of different languages due to high computational resource demands, especially in embedded systems with memory and processor limitations, as existing codebook generation methods are designed for single languages and result in excessive data and processing loads when handling multiple languages.
Innovation Solution
A method for generating a multilingual codebook by iteratively adding code vectors from additional languages to a main language codebook based on distance thresholds, using algorithms like the LBG algorithm and distance measures such as Mahalanobis distance, to create a compact codebook that reduces processor load and memory requirements while maintaining recognition performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate codebooks are used for each language, then speech recognition accuracy for multiple languages is improved, but memory requirements and data volume increase significantly
Solution Approach 1:
The patent merges multiple language-specific codebooks into a single multilingual codebook by selecting code vectors from additional languages that are not already represented in the main language codebook. This combining approach maintains recognition accuracy for multiple languages while reducing the total data volume compared to storing separate codebooks for each language.
Solution Approach 2:
The multilingual codebook serves as a universal data structure that handles multiple languages simultaneously. By making the codebook multi-functional for different languages, the system eliminates the need for separate language-specific codebooks, thereby reducing memory requirements while maintaining recognition capability across languages.
2Reliability
If separate codebooks are used for each language, then speech recognition accuracy for multiple languages is improved, but processor load increases
Solution Approach 1:
The patent combines multiple language processing capabilities into a single unified codebook structure, allowing the processor to handle multiple languages through one data structure rather than switching between multiple separate codebooks. This reduces processor overhead and computational load.
3Power
If a single codebook is used for all languages, then memory requirements and processing load are reduced, but speech recognition accuracy for minority languages deteriorates
Solution Approach 1:
The patent applies local quality by selectively adding code vectors from additional languages only where needed - specifically, code vectors that represent sounds not already covered in the main language codebook. This ensures that minority languages receive adequate representation without uniformly expanding the entire codebook, thereby maintaining accuracy for minority languages while controlling overall data volume.
4Adaptability or versatility
If code vectors from additional languages are added to main language codebook, then multilingual recognition capability is improved, but codebook size increases
Solution Approach 1:
The patent extracts only the necessary code vectors from additional language codebooks - specifically those code vectors that represent phonetic features not already present in the main language codebook. This selective extraction approach adds multilingual capability while minimizing the increase in codebook size by excluding redundant code vectors.
Data Source
AI summary
The present invention relates to a method for generating a multilingual codebook, comprising the steps of providing a main language codebook, providing at least one additional codebook corresponding to a language different from the main language and generating a multilingual codebook from the main language codebook and the at least one additional codebook by adding a sub-set of the code vectors of the at least one additional codebook to the main codebook based on distances between the code vectors of the at least one additional codebook to code vectors of the main language codebook.