Context-Dependent Speech Recognition Library Segmentation
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Solution Overview
Problem
Current voice recognition systems face limitations due to their restricted libraries, leading to either frustration from insufficient words or incorrect matching due to excessive entries, as users must manually add phrases, which is inefficient and prone to errors.
Innovation Solution
Implementing context-dependent speech recognition techniques that dynamically access and prioritize sub-libraries based on speech context, such as location, time, and recipient, to enhance the accuracy of speech recognition without overwhelming the system with unnecessary entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the library contains too few words, then the system cannot process audible commands, but if the library contains too many words, then the system incorrectly matches audio data to translation data
Solution Approach 1:
The patent segments the speech recognition library into multiple context-dependent sub-libraries organized by context categories (location, time, recipient, application). This segmentation allows the system to access only relevant sub-libraries based on the current speech context, effectively reducing the search space and improving matching accuracy while maintaining comprehensive coverage of possible phrases.
Solution Approach 2:
The system dynamically adjusts which sub-libraries are accessed based on the determined context of the speech. The context-dependent library access module selectively accesses relevant sub-libraries rather than searching the entire library, making the system adaptable to different situations and improving recognition accuracy without requiring a monolithic large library.
2Adaptability or versatility
If users manually add phrases to the library, then the library can be customized, but the process is inefficient and prone to errors
Solution Approach 1:
The system automatically learns and updates the context-dependent library by analyzing text-based communications. The automatic learning module extracts phrases from communications and organizes them into appropriate context sub-libraries without user intervention, enabling the system to self-maintain and adapt to new vocabulary and contexts while eliminating manual library maintenance efforts.
Solution Approach 2:
The system continuously learns from text-based communications and updates the context-dependent library based on this feedback. By analyzing actual usage patterns and new phrases from communications, the system automatically refines its recognition capabilities, ensuring the library remains current and accurate without requiring manual updates.
3Adaptability or versatility
If a large quantity of phrases is added to the library, then more speech variations can be recognized, but incorrect matching increases
Solution Approach 1:
Different sub-libraries are created with specialized phrase collections tailored to specific contexts (location, time, recipient, application). Each sub-library contains phrases locally optimized for its context, ensuring high matching accuracy within each context while collectively providing comprehensive phrase coverage across all contexts.
Solution Approach 2:
The large phrase collection is segmented into context-specific sub-libraries, allowing the system to access only the relevant subset for each speech recognition task. This segmentation maintains comprehensive phrase coverage while preventing incorrect matches by limiting the search space to contextually appropriate phrases.
Data Source
AI summary
Techniques and systems are disclosed for context-dependent speech recognition. The techniques and systems described enable accurate recognition of speech by accessing sub-libraries associated with the context of the speech to be recognized. These techniques translate audible input into audio data at a smart device and determine context for the speech, such as location-based, temporal-based, recipient-based, and application based context. The smart device then accesses a context-dependent library to compare the audio data with phrase-associated translation data in one or more sub-libraries of the context-dependent library to determine a match. In this way, the techniques allow access to a large quantity of phrases while reducing incorrect matching of the audio data to translation data caused by organizing the phrases into context-dependent sub-libraries.