Dynamic Speech Dictionary Context Adaptation
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Solution Overview
Problem
Current speech recognition systems face challenges with accuracy due to the use of either large static dictionaries, which are impractical and prone to incorrect identifications, or small dictionaries, which limit recognition quality across varying topics and contexts.
Innovation Solution
Implementing a dynamic dictionary that updates based on context and user interactions through natural language processing, incorporating context mark-up of output text and maintaining a history of communication interactions to adapt the dictionary dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large static dictionary is used to identify a large variety of spoken words, then the variety of recognized words increases, but the accuracy decreases due to presence of similar entries
Solution Approach 1:
The patent implements a dynamic dictionary that adapts its size and content based on the recognition context rather than using a fixed static dictionary. The system adjusts the dictionary dynamically by adding or removing words based on contextual relevance, thereby maintaining high accuracy while preserving the ability to recognize a variety of words when needed.
Solution Approach 2:
The patent applies different dictionary configurations to different recognition contexts. Instead of using a uniform dictionary for all situations, the system tailors the dictionary content to specific local contexts (e.g., domain-specific vocabularies, context-relevant words), ensuring high accuracy for each particular recognition scenario while maintaining overall versatility.
2Measurement precision
If a small dictionary is used to improve accuracy, then the accuracy of word identification increases, but the variety of spoken words that can be identified decreases
Solution Approach 1:
The dynamic dictionary allows the system to expand or contract its vocabulary based on the specific recognition task. When high accuracy is needed for a limited set of words, the dictionary remains small and focused. When broader vocabulary coverage is required, the dictionary expands to include additional context-relevant words, thus balancing accuracy and versatility.
Solution Approach 2:
The patent creates a universal dictionary framework that can serve multiple functions and contexts. The same dictionary infrastructure adapts to different domains, languages, and recognition scenarios by dynamically adjusting its content, making it both accurate for specific tasks and versatile across applications.
3Device complexity
If a static dictionary is used to simplify system implementation, then the device complexity decreases, but the ability to adapt to different contexts and improve recognition quality is limited
Solution Approach 1:
The dynamic dictionary system is self-adjusting, automatically modifying its content based on recognition contexts without requiring manual reconfiguration. The system monitors recognition performance and contextual information, then autonomously updates the dictionary by adding or removing words, reducing the need for complex manual management while enhancing adaptability.
Solution Approach 2:
The patent incorporates feedback mechanisms where recognition outcomes and contextual information are used to continuously refine the dictionary. The system learns from recognition errors and successes, adjusting the dictionary content accordingly, thereby improving adaptability while maintaining manageable complexity through automated feedback-driven updates.
Data Source
AI summary
Techniques related to coding data including techniques for speech recognition using a dynamic dictionary are generally described.