Spoken Natural Language Interface Development System
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Solution Overview
Problem
Developers lack the necessary knowledge and skills in linguistics and computer science to create spoken natural language interfaces from scratch, and existing platforms impose limitations on command usage and application types for voice recognition.
Innovation Solution
A development system that receives seed templates from developers, utilizes crowdsourcing and paraphrasing systems to generate extended templates, and produces statistical language and vector space models for interpreting user commands, enabling the creation of spoken natural language interfaces without requiring advanced technical knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a developer creates an SNL interface from scratch using traditional methods, then the interface can be fully customized and tailored to specific application needs, but the development complexity and required expertise in linguistics and computer science become prohibitively high
Solution Approach 1:
The development system segments the complex SNL interface development process into manageable components: template selection, parameter configuration, and automated model generation. Developers only need to interact with high-level template definitions rather than implementing entire recognition systems from scratch, reducing development complexity while maintaining customization capability.
Solution Approach 2:
The patent introduces an intermediary development system that sits between the developer and the complex underlying SNL technologies. This intermediary automatically handles model training, feature extraction, and system integration based on developer-provided templates, shielding developers from technical complexity while enabling customized interface creation.
2Ease of operation
If existing voice recognition platforms are used, then development time is reduced and ease of use is improved, but restrictions are placed on the commands that can be used and the types of applications that may use the functionality
Solution Approach 1:
The system employs dynamic template definitions that can be adapted to various application domains and command types. Rather than being locked into fixed command structures, developers can define flexible templates that evolve with application requirements, combining ease of use with command flexibility.
Solution Approach 2:
The development system creates a universal template framework that can be applied across different application types and command structures. A single template definition mechanism serves multiple purposes: defining voice commands, specifying recognition parameters, and configuring application-specific behavior, eliminating the need for domain-specific development platforms.
3Reliability
If comprehensive model training and tuning is performed to achieve suitable performance, then recognition accuracy is improved, but the time and resources required for development increase significantly
Solution Approach 1:
The system performs preliminary model training and optimization automatically during the template definition phase. By pre-training models using the provided templates and sample data before deployment, the system achieves high recognition accuracy without requiring developers to invest extensive time in iterative tuning and optimization.
Solution Approach 2:
The development system performs self-service model training and optimization based on the templates provided by developers. The automated system handles feature extraction, model selection, and parameter tuning without requiring manual intervention from developers, maintaining high accuracy while minimizing development time and resource investment.
Data Source
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AI summary
A development system is described for facilitating the development of a spoken natural language (SNL) interface. The development system receives seed templates from a developer, each of which provides a command phrasing that can be used to invoke a function, when spoken by an end user. The development system then uses one or more development resources, such as a crowdsourcing system and a paraphrasing system, to provide additional templates. This yields an extended set of templates. A generation system then generates one or more models based on the extended set of templates. A user device may install the model(s) for use in interpreting commands spoken by an end user. When the user device recognizes a command, it may automatically invoke a function associated with that command. Overall, the development system provides an easy-to-use tool for producing an SNL interface.