Context-Aware Speech Recognition for Keyword-Free Commands
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Solution Overview
Problem
Existing electronic devices require a call keyword for speech recognition, making it inconvenient for users to issue simple commands without repeating the keyword.
Innovation Solution
An electronic apparatus equipped with sensors, memory, and processors that utilize context data to identify commands and perform operations without a separate call keyword by comparing voice commands with stored context data and sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a call keyword is required for speech recognition, then speech recognition can be reliably triggered, but user convenience deteriorates due to the need to repeat the keyword for simple commands
Solution Approach 1:
The system performs preliminary actions by continuously monitoring sensor data (microphone, camera, sensors 110) and comparing it against stored context data 120 before a voice command is even issued. This allows the system to pre-identify situations where simple commands are likely to be given, so that when such commands are detected, the call keyword requirement can be waived. The context data includes information about user behavior patterns, environmental conditions, and device states that are gathered in advance.
Solution Approach 2:
The electronic apparatus serves itself by automatically determining whether a call keyword is necessary based on the analyzed context. The processor 140 autonomously compares real-time sensor data with stored context data, identifies matching situations, and decides whether to activate speech recognition mode without requiring explicit user initiation. This self-service mechanism eliminates the need for users to manually trigger speech recognition for routine commands.
2Ease of operation
If context data and sensor comparison is implemented, then user convenience is improved by enabling keyword-free commands, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system applies partial action by not requiring full speech recognition processing for all commands. Instead, it performs a lighter-weight context comparison using sensor data and stored context patterns. Only when the context match exceeds a certain threshold does the system proceed to full speech recognition. This partial processing approach reduces the average computational load while still providing keyword-free operation for simple commands.
Solution Approach 2:
By pre-storing context data 120 and maintaining continuous sensor monitoring, the system performs preliminary filtering of situations where keyword-free commands are appropriate. This preliminary action reduces the burden on real-time processing, as the complex comparison work is partially done in advance when data is stored and organized for quick retrieval and matching.
3Speed
If speech recognition is activated without call keyword, then operation speed is improved, but accuracy of command identification may worsen
Solution Approach 1:
The system uses feedback from multiple sensor inputs (microphone, camera, sensors 110) to continuously monitor the environment and user behavior. This feedback loop allows the processor 140 to adjust its command identification threshold dynamically. When sensor data strongly indicates a simple command scenario (high feedback confidence), the system can process commands faster with relaxed accuracy thresholds. When feedback is ambiguous, the system maintains higher accuracy requirements, thus balancing speed and precision based on real-time conditions.
Solution Approach 2:
The system applies excessive action in the form of multiple verification checks for borderline cases. When context data matching is partial or uncertain, the system performs additional verification steps rather than immediately executing the command. This ensures that even though the system aims for fast processing, it maintains adequate accuracy by applying more thorough verification only when necessary, rather than uniformly for all commands.
Data Source
AI summary
Disclosed is an electronic apparatus which performs speech recognition. The apparatus includes: at least one sensor; a memory storing context data each corresponding to a plurality of commands; a microphone; and at least one processor configured to acquire a text corresponding to a voice command in case that the voice command is received through the microphone, identify context data corresponding to an identified command in case that a command having similarity of a first threshold value or more with the acquired text is identified among the plurality of commands stored in the memory, acquire a control signal corresponding to the identified command in case that current context data including context data acquired through the at least one sensor is identified as having similarity of a second threshold value or more with the context data corresponding to the identified command, and perform an operation based on the control signal.