Adaptive Misrecognition in Conversational Speech Interfaces
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Solution Overview
Problem
Current systems fail to provide a complete environment for users to submit natural language queries and commands through speech and non-speech interfaces, as they are not robust enough to handle imperfect information and context, leading to incomplete or ambiguous responses.
Innovation Solution
A system that uses a combination of speech and non-speech interfaces, coupled with cognitive models and adaptive misrecognition analysis, to parse and interpret natural language inputs, incorporating context, domain knowledge, and user profiles to generate accurate and natural responses, while accommodating partial failures through probabilistic and fuzzy reasoning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speech recognition systems are used to process natural language queries, then user interaction becomes more natural and convenient, but the system fails to reliably handle imperfect information such as incomplete sentences, slang terminology, and word variations
Solution Approach 1:
The system performs preliminary actions by pre-defining multiple possible interpretations and responses for potential user queries. The conversational model is prepared in advance with various speech patterns, slang terms, and incomplete sentence structures, allowing the system to reliably match and respond to imperfect user input without requiring perfect grammar or complete sentences.
Solution Approach 2:
The system changes parameters by dynamically adjusting the interpretation thresholds and matching criteria based on the context of the conversation. Instead of requiring exact matches, the system modifies its recognition parameters to accommodate variations in speech patterns, allowing it to maintain reliability while processing natural, imperfect human language.
2Reliability
If the system attempts to provide complete and accurate responses to all natural language queries, then response quality improves, but the system complexity increases due to the need to handle context, domain knowledge, and multiple failure scenarios
Solution Approach 1:
The system segments the complex task of natural language processing into distinct functional components: a conversational speech analyzer for initial interpretation, a contextual knowledge base for domain-specific information, and a response generation module. This segmentation allows each component to specialize in specific aspects of processing, reducing overall system complexity while maintaining complete and accurate responses.
Solution Approach 2:
The patent introduces an intermediary conversational model that acts as a mediator between the user's imperfect input and the system's processing requirements. This intermediary layer translates natural language variations into structured queries, simplifying the downstream processing and reducing the complexity of handling context and domain knowledge directly in the core system.
3Reliability
If the system uses strict speech recognition protocols to ensure accurate command interpretation, then command execution reliability improves, but the system becomes less adaptable to variations in user speech patterns and natural language expressions
Solution Approach 1:
The system implements dynamic speech recognition protocols that adapt in real-time based on the conversational context. The recognition strictness is adjusted dynamically - being more lenient with slang and incomplete sentences in casual contexts, while maintaining higher accuracy requirements for critical commands. This dynamic approach allows the system to maintain both reliability and adaptability across different interaction scenarios.
Data Source
AI summary
A system and method are provided for receiving speech and/or non-speech communications of natural language questions and/or commands and executing the questions and/or commands. The invention provides a conversational human-machine interface that includes a conversational speech analyzer, a general cognitive model, an environmental model, and a personalized cognitive model to determine context, domain knowledge, and invoke prior information to interpret a spoken utterance or a received non-spoken message. The system and method creates, stores and uses extensive personal profile information for each user, thereby improving the reliability of determining the context of the speech or non-speech communication and presenting the expected results for a particular question or command.