Speech-Based Automation Control via Location-Aware Model Selection
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Solution Overview
Problem
Conventional user interfaces for automation systems are complex and difficult to use, leading to errors in command issuance and adverse impacts on processes and devices, due to the need for users to navigate multiple interfaces and device-specific controls.
Innovation Solution
A speech-based user interface that selects a topic-specific speech recognition model based on the user's location to accurately identify and execute commands or queries relevant to the proximate automation device, using a combination of acoustic and language models trained for specific automation systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional user interfaces are used for automation systems, then device control functionality is provided, but user interface complexity increases and ease of operation deteriorates
Solution Approach 1:
The system segments the automation control interface by spatial location, dividing the facility into multiple zones with dedicated speech recognition models for each zone. This allows users to interact with only the relevant local devices rather than navigating a complex global interface, resolving the contradiction between providing comprehensive control functionality and maintaining interface simplicity.
Solution Approach 2:
The speech recognition system acts as an intermediary between the user and the automation devices. Instead of requiring users to directly navigate complex device interfaces, speech commands serve as a natural language mediator that translates user intent into device control actions, significantly improving ease of operation without adding interface complexity.
2Reliability
If conventional user interfaces require navigation of multiple interfaces and device-specific controls, then comprehensive device control is achieved, but cognitive burden increases leading to errors
Solution Approach 1:
The system implements local quality by training speech recognition models with topic-specific vocabulary and language patterns relevant to each local automation device or zone. This localized approach ensures high recognition accuracy for device-specific commands without requiring users to navigate complex interfaces, thereby improving reliability while maintaining ease of operation.
Solution Approach 2:
The speech recognition system provides self-service by automatically adapting to the user's location and selecting the appropriate recognition model without requiring manual configuration. This eliminates the cognitive burden of selecting interfaces or modes, allowing users to simply speak their commands naturally, which improves both reliability and ease of operation.
3Measurement precision
If topic-specific speech recognition models are selected based on user location, then speech recognition accuracy improves, but system complexity increases
Solution Approach 1:
The system implements dynamics by making the speech recognition model selection adaptive and location-dependent. Instead of using a single static model, the system dynamically selects from multiple topic-specific models based on the user's current location detected via GPS or other positioning mechanisms. This dynamic adaptation improves recognition accuracy without requiring complex manual configuration, as the selection process is automated based on spatial context.
Data Source
AI summary
Systems and methods for speech-based monitoring and/or control of automation devices are described. A speech-based method for monitoring and/or control of automation devices may include steps of determining a type of automation device to which first speech relates based, at least in part, on a location associated with the first speech; selecting a topic-specific speech recognition model adapted to recognize speech related to the determined type of automation device; using the topic-specific speech recognition model to recognize second speech provided at the location, wherein recognizing the second speech comprises identifying a query or command relating to the type of automation device and represented by the second speech; and issuing the query or command represented by the second speech to an automation device of the determined type.