MR Scanner Speech Control via Dynamic Command Library
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Solution Overview
Problem
Current MR scanner operations require manual input by medical technicians, leading to inefficiencies and potential hygiene issues, as settings need to be made through a separate control room interface, and the noisy environment complicates speech recognition.
Innovation Solution
A speech control system that dynamically generates a library of speech commands based on the MR scanner's current status, using a microphone for acoustic input, an analog-to-digital converter, and a speech recognition algorithm to create control commands for the scanner, with an optional online module for improved recognition reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speech control is implemented in the noisy MR scanner environment, then hands-free operation and hygiene are improved, but speech recognition reliability deteriorates due to background noise
Solution Approach 1:
The system dynamically adapts the speech recognition process by adjusting the command library based on the current system status. The command library is not static but is dynamically generated to include only status-relevant commands, making the recognition process more robust against noise by reducing the search space and focusing on contextually appropriate commands.
Solution Approach 2:
The system changes the parameter of the command library from a complete set of all possible commands to a filtered subset based on current system status. This parameter change (from full library to status-based subset) improves signal-to-noise ratio in speech recognition by reducing the probability of false recognition and enabling more focused acoustic pattern matching.
2Adaptability or versatility
If a complete library of all speech commands is always available, then command coverage is improved, but computing power requirements and recognition time increase
Solution Approach 1:
The system extracts only the relevant subset of commands from the complete command library based on the current system status. Instead of processing all possible commands, the system takes out and processes only those commands that are applicable to the current state, significantly reducing computational load while maintaining full adaptability when needed.
Solution Approach 2:
The command library is segmented into multiple status-specific subsets rather than being a single monolithic structure. Each system status has its own tailored command subset, allowing the system to process smaller, more manageable segments of commands rather than the entire library, reducing memory access time and processing requirements.
3Productivity
If status-based command library filtering is implemented, then speech recognition speed is improved, but system complexity increases
Solution Approach 1:
The system uses feedback from the current system status to dynamically configure the command library. The status information feeds back into the speech recognition system to adjust which commands are available, creating a closed-loop control mechanism that automates the filtering process and reduces manual configuration complexity.
Solution Approach 2:
The speech recognition system is designed with multi-functionality to handle both full-library and status-filtered modes. The same recognition engine can operate with different command library configurations depending on system status, making the system universally adaptable without requiring separate recognition systems for different operating conditions.
Data Source
AI summary
A system for performing magnetic resonance tomography is disclosed. A control system creates a speech data stream from an acquired linguistic expression and generates a command library, which contains a selection of speech commands, to each of which one or more linguistic expressions are assigned. The selection of speech commands is loaded from a command database depending on a current system status of a magnetic resonance (MR) scanner. The control system applies a speech recognition algorithm to the speech data stream to determine whether a linguistic expression contained in the command library can be assigned to the speech data stream. If so, the acquired linguistic expression is recognized, a speech command from the command library assigned to the recognized linguistic expression is established, and a control command for controlling the MR scanner in accordance with the speech command is created.